Social-P2P: An Online Social Network Based P2P File Sharing System

Transcription

1 1.119/TPDS , IEEE Trasactios o Parallel ad Distributed Systems 1 : A Olie Social Network Based P2P File Sharig System Haiyig She*, Seior Member, IEEE, Ze Li, Studet Member, IEEE, Kag Che Abstract A peer-to-peer (P2P) file sharig system provides a platform that eables a tremedous umber of odes to share their files. Retrievig desired files efficietly ad trustworthily is critical i such a large ad jumbled system. However, the issues of efficiet searchig ad trustworthy searchig have oly bee studied separately. Simply combiig the methods to achieve the two goals doubles system overhead. I this paper, we first study trace data from Facebook ad BitTorret. Guided by the observatios, we propose a system that itegrates a social etwork ito a P2P etwork, amed, for simultaeous efficiet ad trustworthy file sharig. It icorporates three mechaisms: (1) iterest/trust-based structure, (2) iterest/trust-based file searchig, ad (3) trust relatioship adjustmet. By exploitig the social iterests ad relatioships i the social etwork, the iterest/trust-based structure groups commo-multi-iterest odes ito a cluster ad further coects socially close odes withi a cluster. The comparably stable odes i each cluster form a Distributed Hash Table (DHT) for iter-cluster file searchig. I the iterest/trust-based file searchig mechaism, a file query is forwarded to the cluster of the file by the DHT routig first. The, it is forwarded alog costructed coectios withi a cluster, which achieves high hit rate ad reliable routig. Moreover, sharig files amog socially close frieds discourages odes from providig faulty files because people are ulikely to risk their reputatio i the real-world. I the trust relatioship adjustmet mechaism, each ode i a routig path adaptively decreases its trust o the ode that has forwarded a faulty file i order to avoid routig queries towards misbehavig odes later o. We coducted extesive trace-drive simulatios ad implemeted a prototype o PlaetLab. Experimetal results show that achieves highly efficiet ad trustworthy file sharig compared to curret file sharig systems ad trust maagemet systems. Idex Terms P2P etworks, Olie social etworks, File sharig. 1 INTRODUCTION Peer-to-peer (P2P) systems are widely used i file sharig applicatios, such as BitTorret. More tha 5% of the files dowloaded ad 8% of the files uploaded o the Iteret are through P2P etworks [1]. P2P file sharig systems attract millios of users. Due to the largescale of the P2P systems, efficietly locatig a desired file has bee a ope problem for may years. Cosiderig the umerous users without preexistig trust relatioships i the P2P ope platform, providig trustworthy file sharig has become aother importat issue. Ideed, may users foud themselves dowloadig the wrog files due to misleadig file ames ad descriptios. Peers with malicious itet upload faulty files, defied as tampered files or files with malicious code (e.g., Troja horses ad viruses). * Correspodig Author. sheh@clemso.edu; Phoe: (864) ; Fax: (864) Haiyig She, Ze Li ad Kag Che are with the Departmet of Electrical ad Computer Egieerig, Clemso Uiversity, Clemso, SC, {sheh, zel, kagc}@clemso.edu The research o the issues of efficiet ad trustworthy P2P file searchig has bee coducted separately. Although may methods have bee proposed to ehace the efficiecy or trustworthiess, the idividual methods are ot efficiet by themselves. I order to improve the search efficiecy, some works cluster the odes with the same iterest to icrease file hit rate [2] [14]. Sice these methods cluster odes with a sigle iterest, a ode with multiple iterests eeds to maitai multiple clusters, which geerates a high overhead for cluster maiteace ad iter-cluster searchig. Also, most of the previous approaches deped o the cotets i users local storage to ifer their file iterests. They are ot oly costly but also uable to retrieve the complete iterests of a user with isufficiet stored cotets, such as ew users or the users that have deleted shared files. A widely-used solutio for trustworthy file sharig is to employ a cyber-trust maagemet system [15] [2], i which each ode rates service providers based o the service quality. However, accumulatig sufficiet ratigs for calculatig a accurate trust may take a log time. Also, periodical trust updates produce a high overhead. Curretly, the oly approach to achieve both efficiet ad trustworthy P2P file searchig is to directly combie a system for high search efficiecy ad a cyber-trust maagemet system. I this way, all odes eed to maitai structures for two systems, which would double the cost, thus makig the high overhead problem eve more severe. Therefore, a system that ca simultaeously provide both efficiet ad trustworthy file sharig with low overhead is greatly eeded. I this paper, we propose a system that itegrates a social etwork ito a P2P etwork, amely, to simultaeously achieve efficiet ad trustworthy P2P file sharig by leveragig social iterests ad relatioships. Two facts lay the foudatio for this work. First, people usually share files that they are iterested i [11]. Iterests idicated by a user himself i his profile ca more accurately reflect the complete iterests of the user. Secod, users are ulikely to provide faulty files to their socially close frieds because it will impair their social relatioships with others ad degrade their reputatio i their social commuities i the real world. Thus, by mappig the P2P cyber etwork to the social etwork ad restrictig cyber services (e.g., file sharig ad message routig) betwee socially close odes, misbehaviors (i.e., providig faulty files ad rejectig forwardig messages) ca be discouraged. I this paper, we first study trace data from Facebook (c) 213 IEEE. Persoal use is permitted, but republicatio/redistributio requires IEEE permissio. See

2 1.119/TPDS , IEEE Trasactios o Parallel ad Distributed Systems ad BitTorret. We gai a umber of observatios (O): O1: Some iterests are highly correlated. That is, give a pair of correlated iterests A ad B, if a perso has iterest A, (s)he is very likely to also have iterest B. O2: A olie social etwork user has differet cotact frequecy with differet users. O3: Frieds i a olie social etwork (users with direct social etwork coectios) usually have very close social relatioships i their real life. O4: A P2P file sharig system possesses a certai percet of comparably stable odes. O5: The popularity distributio of iterests ca be modeled as a Zipf distributio; most of the file queries are for a small percet of file iterests. Guided by these observatios, we develop the followig three compoets for : (1) Iterest/trust-based structure costructio. It groups commo-multi-iterest odes ito a iterest cluster (O1), ad forms comparably stable odes ito a Distributed Hash Table (DHT) to coect clusters for efficiet iter-cluster data sharig (O4). Withi each iterest cluster, odes are coected with their socially close odes as P2P overlay eighbors (O3). Furthermore, uses aoymous routig to prevet malicious odes from selectively attackig socially distat odes ad protects the privacy of the odes. (2) Iterest/trust-based file searchig. The trustworthiess betwee odes is weighted ad a ode teds to forward a file query to trustworthy eighbors (O2). Sice higher popularity files have more file copies beig shared i the system, radom walk is employed for high hit rate i itra-cluster file searchig cosiderig higher popular files have more copies i the system (O5). We further propose routig algorithms to ehace the radom-walk based routig. (3) Trust relatioship adjustmet. Each ode i a routig path decreases its trust o the ext hop whe a faulty file is retrieved i order to avoid routig queries towards misbehavig odes later o (O2). With these three compoets, achieves highly efficiet ad trustworthy file sharig with low overhead. We preset the details below. (1) High efficiecy: Clusterig commo-multi-iterest odes eables odes to quickly fid files i its ow cluster. The higher-level DHT eables efficiet itercluster search, which helps odes to fid files outside of their iterests quickly. (2) High trustworthiess: Cofiig services betwee socially close odes discourages odes from providig faulty services. Trust-based radom walk ca esure a query message be forwarded amog trustworthy odes. The trust decrease upo misbehavior ca quickly isolate misbehavig odes. The aoymous routig further reiforces the trustworthiess of file sharig. (3) Low overhead: Low overhead i structure maiteace. Comparatively stable odes form a DHT ad other dyamic odes costitute a ustructured P2P by oly coectig to their socially close odes, leadig to low P2P overlay maiteace without frequet DHT maiteace i ode dyamics. Low overhead i efficiet file sharig. Clusterig commo-multi-iterest odes rather tha commosigle-iterest odes reduces the cluster maiteace overhead, ehaces file search hit rate withi clusters, ad reduces the iter-cluster searchig overhead. Low overhead i trustworthy file sharig. does ot eed to periodically accumulate ratigs of each ode to calculate its trust. Each ode oly eeds to maitai its eighbors trusts, resultig i low overhead for trust maagemet. As far as we kow, this is the first work that simultaeously cosiders both efficiet ad trustworthy file queryig with low overhead i P2P etworks. Like some previous P2P works [21] [26], has a cetral server, which is maily used to hadle the social etwork fuctios ad assistace work. The remaider of the paper is orgaized as follows. Sectio 2 gives a overview o the existig file search systems ad reputatio systems. Sectio 4 describes the desig of. The performace evaluatio is preseted i Sectio 5. Sectio 6 cocludes the paper. 2 RELATED WORK Efficiet file sharig. Numerous methods icludig locality-aware searches [5], [6] ad social etwork based searches [7] [12], [14], [27] have bee proposed i hopes of icreasig the search efficiecy i P2P systems. Searchig based o social etworks ca be classified ito two categories: ustructured etworks ad DHTs. I the ustructured etwork based search, Carchiolo et al. [27] ad Lei et al. [1] proposed to gradually cluster odes ito the same group if they query or reply for the same resources. Fast et al. [11] proposed to use hierarchical Dirichlet filterig to extract user prefereces to musical styles from their music libraries. They cluster the available files i the etwork based o user iterests. Although these algorithms ca improve the basic search algorithm, sice the odes with the same iterests ca be grouped oly after they have iteractios, the clusterig process takes a log time. By relyig o the social iterest iformatio, ca quickly ad accurately cluster odes with similar iterests. I the category of DHTs, Li et al. [12] proposed Cyber, i which the odes are associated with certai commuities based o their iterests. Cyber builds a DHTbased idex o the keywords of items. Whe a ode queries for a item, the DHT-idexed peer resposible for the queried keyword returs the items that match the iterests of the commuity the requester belogs to. Zhag et al. [14] proposed to improve search i ustructured P2P overlay etworks by buildig a partial idex of globally upopular data ad o-major iterest data based o a DHT. The idex ca assist peers i fidig other peers with similar iterests ad provide search hits for a data difficult to be located by explorig peer iterests. The curret DHT-based social etwork eables fast ode clusterig but suffers from high system maiteace overhead i chur. Meawhile, sigle iterestbased ode clusterig requires each ode to maitai several clusters, which leads to high cluster maiteace overhead. I, each ode oly eeds to maitai a sigle multi-iterest cluster. Moreover, oly comparatively stable odes i these clusters form a stable DHT ad the dyamic odes oly maitai a umber (c) 213 IEEE. Persoal use is permitted, but republicatio/redistributio 2 requires IEEE permissio. See

3 1.119/TPDS , IEEE Trasactios o Parallel ad Distributed Systems of frieds i the ustructured cluster. Thus, geerates a low system maiteace overhead. Trust maagemet. Numerous reputatio systems [13], [15] [2] have bee proposed to icrease the trustworthiess of the P2P systems. The basic idea is to let peers rate their service providers after each receivig the service. The accumulated ratig of a ode is used to represet its trustworthiess. However, accumulatig sufficiet ratigs to calculate a accurate reputatio value takes a log time. Also, maagig the ratigs betwee odes ad calculatig the reputatio value for each ode geerate high overhead. takes advatages of social trust relatioships betwee people to icrease the trustworthiess of file sharig at lower overhead. Marti et al. [13] ivestigated how existig social etworks could beefit P2P data etworks by leveragig the iheret trust associated with social liks for DHT routig. This work oly deals with misroutig problems, while targets more geeral file trustworthiess problem. Galuba et al. [28] leveraged social etworks to avoid free-riders i BitTorret. Frey [29] addresses the eed for trust i user-cetric applicatios by proposig two distributed protocols that combie iterest-based coectios betwee users with explicit liks obtaied from social etworks. 3 TRACE DATA ANALYSIS I this sectio, we aalyze Facebook trace data crawled by us ad BitTorret trace data from the Graffiti Network Project [3]. The Facebook trace data covers the iterests of 32,344 users i the South Carolia Regio i Jue, 21. To crawl the data, we selected two users with o social relatioship i Clemso Uiversity as seed odes ad built a fried graph usig breadth first search through each ode s fried list. We skipped the users whose persoal iformatio caot be accessed. Fially, we drew a social etwork graph, where a vertex is a user ad a lik meas these two users are frieds. The average umber of frieds per ode is ad the average path legth of the graph is The BitTorret user traffic was collected durig a three week period (Oct 28, 28-Nov 21, 28) ivolvig 3,57,588 odes. Iterest clusterig. We parsed the iterest iformatio from the users profiles i Facebook. We removed the iterests irrelevat to file sharig (e.g., sleep ad shoppig ). We classified the remaiig iterests (e.g., actio movie, classic music ad sports ) ito 18 categories. We plotted a graph G(V, E) to show the relatioship amog the 18 iterests. The vertices V are the iterests. A lik E betwee V 1 ad V 2 idicates the co-existeces of both V 1 ad V 2 i all profiles of t persos, where t is the threshold of the umber of persos. Figure 1 plots the graphs with threshold t=1 ad t = 5, respectively. Whe t = 1, the iterests are desely coected. Whe t = 5, several iterests are still clustered, while two iterests are isolated. Also, the umber of iterests i oe iterest cluster varies. Observatio(O)1: Some iterests are highly correlated. That is, give a pair of correlated iterests A ad B, if a perso has iterest A, (s)he is very likely to also have iterest B. Iferece(I)1: Istead of clusterig the odes based o (a) Threshold = 1 TV Shows Pets & Aimals Gamig Pop Music Classical Music Sports Cartoo Rock Music Rap Music Fig. 1. The clusterig feature of iterests. Travel Educatio News & Politics Auto &Vehicles Comedy Actio People & Blogs Movie Sciece Fictio Romace Movie (b) Threshold = 5 each iterest, which leads to high overhead, clusterig commo-multi-iterest odes ca improve file retrieval efficiecy ad reduce cluster maiteace overhead. CDF of percet of frieds of a perso Reply rate Fig. 2. Closeess distributio. Percet of postigs 9% 8% 7% 6% 5% 4% 3% 2% 1% % Familiar persos Ufamiliar persos Fig. 3. Postig distributio. Closeess betwee olie users. We aalyzed the reply rate of the posts o user commet walls ad pictures i Facebook. If user B posted M commets to user A, ad A replied m commets, we defie m/m as the reply rate of user A to his fried B. We the calculated the average value of each user s reply rates to his/her frieds ad used it to represet the closeess amog users. Figure 2 shows the distributio of the average reply rates of all users to their frieds. It shows that a user has a reply rate of less tha.7 to almost 9% of its frieds o average. That is, for oly 1% of frieds, a perso replies more tha 7% of their commets. The results idicate that users treat differet persos i a olie social etwork differetly. I order to show that the behavior of replyig commets is drive by social closeess of odes rather tha the cotets of the commets (e.g. iterestig commets), we further ivestigate the commet postig behaviors betwee people. Give a postig from user A to user B, we call it postig for a familiar perso if B has posted commets o A s wall/picture ad A replied B s commet before. Otherwise, we call it postig for a ufamiliar perso. We calculated the percet of postigs for (u)familiar persos for each user, ad plotted the average values i Figure 3. We see that 83% of a perso s postigs are for familiar persos. Combig these results, we observe: O2: A user i the olie social etwork has differet cotact frequecy with other differet users. We reasoably assume the user cotact frequecy idicates the trust betwee them, we ca ifer that: I2: The trust relatioship betwee odes should be weighed. Retrievig files from trustable odes ca icrease the trustworthiess of the retrieved files. Figure 4 further shows the social relatioship betwee the users. We observe that: O3: Frieds i a olie social etwork usually have very close social relatioships i their real life. I3: Requestig services (e.g., providig files ad query (c) 213 IEEE. Persoal use is permitted, but republicatio/redistributio 3 requires IEEE permissio. See

4 1.119/TPDS , IEEE Trasactios o Parallel ad Distributed Systems routig) from socially close odes ca ehace the trustworthiess of received services, sice people do ot wat to rui their reputatio i real life. Same church Kiship Others Colleague Classmate CDF of percet of odes Number of torret Node olie duratio (s) Fig. 4. Social relatioship. Fig. 5. Node chur rate. Node stability. We ow aalyze the distributio of ode stability i the BitTorret file sharig system. Figure 5 shows the cumulative distributio fuctio (CDF) of the legth of time that odes remai olie. O4: A P2P file sharig system possesses certai percetages of comparably stable odes (15%) ad highly dyamic odes (2%). I4: Buildig a DHT usig all odes i the system is ot suitable for P2P file sharig due to high chur. Formig the comparatively stable odes ito a DHT to assist other odes i file retrieval ca ehace file sharig efficiecy. File iterest popularity. The umber of torrets of a file category (i.e., iterest) represets its popularity. We raked 55 iterests based o the umber of torrets. The iterest with rak has the largest umber of torrets. Figure 6 shows the umber of torrets of a iterest versus its rak i the Measured Torrets 2 Zipf log-log scale. It also icludes Rak of a iterest a lie for the best fit Zipf Fig. 6. File popularity distributio. distributio. O5: The popularity distributio of iterests ca be modeled as a Zipf distributio. I the radom walk file searchig algorithm, a ode radomly selects oe or several eighbor odes (except the ode which forwards the message) as the ext hops for message forwardig. I5: I the case that higher popularity files have more file copies beig shared i the system, the radom walk file searchig algorithm ca achieve high hit rate i retrievig popular files. 4 THE DESIGN OF SOCIAL-P2P The above observatios ad ifereces motivate us to itegrate a social etwork ito a P2P etwork for efficiet ad trustworthy file retrieval. Thus, we propose Social- P2P, which leverages the social closeess ad iterest iformatio i the social etwork to eable odes with a social relatioship ad multiple commo iterests share files betwee each other. Figure 7 shows the system structure of. Based o I1, we group commo-multi-iterest odes together ito a iterest cluster. Based o I2 ad I3, withi each iterest cluster, odes are coected based o their social etwork liks. The trustworthiess betwee odes is weighed ad a ode teds to forward a file query to higher trustworthy eighbors i file searchig. Based o I4, we select a comparably stable ode as a ambassador Social etwork layer P2P layer Commo multi iterest odes Iterest cluster Ambassador Registratio server DHT A Comparably stable odes B C Iterest cluster Fig. 7. A overview of the system structure. for its ow cluster, ad form all ambassadors to a DHT for efficiet iter-cluster data sharig. Like BitTorret, eables odes to share their dowloaded files with others. Thus, based o I5, a ode uses radom walk i itra-cluster searchig. 4.1 Iterest/Trust-based Structure Costructio umerically represets iterests of a ode based o the Vector Space Model (VSM) [31]. The iterests are predefied based o the particular applicatio. For example, for a geeral-purpose file sharig system such as BitTorret, the iterest pre-determiatio ca be like that i Yahoo! Aswers ad YouTube. A file sharig system o a campus ca use the major ames as the iterests. It provides a iterest dictioary vector, which cosists of all the iterests (m). Each ode compares its ow iterests with the iterest dictioary vector as show i Figure 8. If it has a iterest i the vector, the correspodig positio of the vector is set to 1. Otherwise, the positio is set to. Fially, each ode i has a iterest vector v i, which is a biary vector with m dimesios. Iterest Item Health Pets Sports Sciece Arts Educatio Society Peer ID Fig. 8. Example of a iterest vector. We use the Hilbert curve techology [32] to cluster commo-multi-iterest odes with similar iterest vectors. The Hilbert curve coverts a multidimesioal iterest vector to a oe-dimesioal Hilbert value, so that the closeess of the Hilbert values idicates the closeess of the iterest vectors, i.e., the similarity betwee odes iterests. We use (Max) H max to represet the ( 1) Max Max theoretical largest Hilbert value, which depeds ( 2) Max 2 Max o the vector dimesio. Assume we build clusters with ID [, 1]. I the 3 Max 5 Max 4 Max case that the Hilbert values are uiformly distributed i Fig. 9. Hilbert clusterig the space of [, H max 1], vector. the [, H max 1] is uiformly divided to itervals as show i Figure 9. A ode with Hilbert umber [ (b 1) Max, b Max ) should be i cluster (b 1). I the case that the Hilbert values are ot uiformly distributed i the space of [, H max 1], we ca divide the space [, H max 1] to itervals based o the desity of the distributio of Hilbert values. A ode ca idetify its cluster accordig to its geerated Hilbert value. Other clusterig methods [33], [34] ca be used to improve the clusterig accuracy but at the cost of higher overhead (c) 213 IEEE. Persoal use is permitted, but republicatio/redistributio 4 requires IEEE permissio. See

5 1.119/TPDS , IEEE Trasactios o Parallel ad Distributed Systems I our future work, we will study the appropriate legth of the vector ad the graularity of a iterest for effective commo-multi-iterest clusterig. Next, we study the distributio of the umber of odes i a cluster. Usig the Hilbert clusterig mechaism, we clustered the users i the Facebook trace data based o their iterests for compariso. We also used the mechaism to cluster odes with radomly distributed iterests. Recall that the Facebook trace has 18 iterests. To geerate the radom distributio, we assiged each ode a certai umber of iterests radomly selected from the 18 iterests (the umber is radomly picked from [,18)), ad clustered the odes based o their Hilbert values. We raked the clusters by the umber of odes i each cluster. The cluster with the largest umber of odes has the highest rak. Figure 1 shows the distributio of the umber of odes i each cluster versus the cluster rak. It shows that the umber of odes i each cluster coforms to a power law distributio, while the umber of odes i radom iterest distributio exhibits a small variace. The highly skewed distributio idicates that the iterests of people are ot radomly distributed but have certai correlatio, which is cosistet with Figure 1. Figure 11 shows the average, maximum ad miimum umber of social frieds of each perso (degree) i its iterest cluster. The average degree rages from [2.1,18.2], the maximum degree rages from [6,223] ad the miimum degree rages from [,1]. Therefore, i most cases, a ode has frieds i its ow commo-multiiterest cluster. The average umber of frieds of each ode i our data set is Therefore, most odes have frieds i other clusters. Thus, we observe: O6: I most cases, a ode has frieds ot oly i its ow commo-multi-iterest cluster but also i other clusters. I6: I most cases, each ode ca establish liks with its frieds i its ow iterest cluster, ad ca ask a fried i aother iterest cluster to forward a query to that cluster. Each cluster of commo-multi-iterest odes has a ambassador, which is a comparably stable ode ad is resposible for the iter-cluster file searchig. Like curret olie social etworks, has a server maagig ode registratio ad ambassadors. The priciple of stable ode selectio is that the loger time a ode is olie daily i a P2P etwork, the higher probability it will stay i the etwork [35]. Iitially, the server is the ambassador for each cluster. Whe a ode s olie time exceeds a pre-defied threshold, it reports to the server for the promotio to a ambassador. The server desigates the ode as the ambassador for the cluster if the server is the ambassador. Otherwise, the ode becomes a backup ambassador. Whe a ambassador departs volutarily, it otifies the server. I the overlay stabilizatio, whe aother ambassador otices the abrupt departure or failure of a ambassador, it otifies the server, which selects a alive backup ambassador to replace the leavig ambassador. Each user is required to submit his iterest iformatio ad social iformatio i registratio. Iterest iformatio, such as leisure prefereces ad religious beliefs, is used for commo-multi-iterest ode clusterig. Social iformatio, such as residece, educatio ad employmet, is used to build social liks betwee the odes withi Number of odes i a cluster Real trace from Facebook Ramdo iterest distributio Power law distributio Rak of clusters Fig. 1. Number of odes i a iterest cluster. Average degree Max Avg. Mi C1 C3 C5 C7 C9 C11 C13 C15 C17 C19 C21 C23 C25 C27 C29 Fig. 11. Average # of social frieds of a ode i a iterest cluster. a cluster. After a ode registers, it calculates its Hilbert umber, ad the gets the ambassador(s) of its iterest clusters from the server. Based o the social iformatio of the ode, servers recommed frieds to the ode, such as classmates i the same college, colleagues i the same compay ad etc. The ode selects familiar frieds from the recommedatio ad adds them ito its social fried list. It builds overlay liks to the frieds belogig to its iterest clusters. These coected frieds become its multi-iterest eighbors. If the umber of fried odes is less tha a threshold T h d, the ode requests its frieds to recommed their trustworthy frieds i the same iterest clusters as itself. The ode coects to the recommeded odes as overlay eighbors i the P2P layer based o the frieds-of-frieds (FOF) relatioship. For example, i Figure 7, ode A coects to ode C recommeded by its fried B as P2P overlay eighbor. As a result, a ode s overlay eighbors i iclude its 1-hop frieds ad 2-hop FoFs. Queryig files from these eighbors esures trustworthy file sharig, sice users possessig a social etwork primarily iteract with 2 to 3 hop parters i real life [36]. If a ode has already registered i the system before, whe it logs i, it directly coects to its previous overlay eighbors. Whe a ode leaves the system, it eeds to otify its eighbors ad the server. After beig registered, users ca add or delete iterests i their profiles later o. The, their iterest clusters are updated accordigly. 4.2 Iterest/Trust-based File Searchig Itra-cluster routig algorithm A queried file has oe or multiple iterests. If at least oe query iterest belogs to the file requester s iterests, it uses the itra-cluster routig algorithm that forwards a query to trustworthy odes. We defie the social distace betwee two odes as the umber of hops i the shortest path betwee them i the social etwork. As idicated i [37], a reductio i social distace sigificatly icreases trust betwee odes. Thus, we use a expoetial model to reflect the relatioship betwee trust ad social distace. Specifically, the trust weight of ode i o j deoted by w(i, j) is calculated by: w(i, j) = e ( li,j 1), (1) where l i,j is the social distace betwee odes i ad j i the social etwork ad -1 is used for ormalizatio so that the weight of the closest odes is 1. Each ode employs radom walk search, i which a query message is forwarded to oe or several radomly chose P2P etwork eighbors at each hop util the desired file is foud. To avoid repetitive searchig paths, a ode does ot further forward a query if it has received this query previously. A ode s P2P etwork eighbors (c) 213 IEEE. Persoal use is permitted, but republicatio/redistributio 5 requires IEEE permissio. See

6 1.119/TPDS , IEEE Trasactios o Parallel ad Distributed Systems are trustworthy sice they are frieds or FoFs of the odes i their social etworks. Frieds should have higher probability tha FoFs to be chose as forwarders because they are relatively more trustworthy. Thus, for a ode i with d P2P etwork eighbors, the probability of a eighbor j beig selected from the eighbor set N i of i as the message forwardig ode is p(i, j) = w(i, j)/ j N i w(i, j). (2) The message is radomly forwarded withi the cluster with a time to live (TTL). After a forwardig, the TTL of the message is reduced by 1. The query process is termiated whe TTL= or whe the desired file is foud Iter-cluster routig algorithm Iter-cluster queryig is eeded whe users eed to query files outside their iterests or whe the query i the curret cluster caot be satisfied. I this case, usig the way described i Sectio 4.1, based o the multiple iterests of the queried file, the requester geerates a query vector as geeratig its iterest vector: calculate its Hilbert value ad get the ID of the cluster mapped to the Hilbert value. Sice the cluster ID represets the commo-multi-iterests of the odes i the cluster, the mapped cluster is the destiatio cluster that holds the requested file. Accordig to I6, the requester first asks its frieds i its fried list whether they belog to the destiatio cluster. If yes, the file request is set to the cluster through the fried. Otherwise, the requester relies o the ambassador i its curret cluster to forward the file request. I this way, the traffic from ambassador ca be greatly reduced. Usig DHT routig, the request is forwarded to the ambassador i the destiatio cluster. After the file request arrives at the destiatio cluster, it is forwarded by the itra-cluster routig algorithm. Two similar vectors may be divided ito two eighborig clusters. For example, a vector with Max has cluster ID=1, while a vector with Max 1 has cluster ID=. Therefore, if a query caot be satisfied withi the destiatio cluster, it is forwarded to the eighborig clusters i both clockwise ad couter-clockwise directio with a Cluster TTL (CTTL). Similar to iter-cluster routig, a ode tries to sed the query via its frieds i the social etwork rather tha ambassadors i the DHT. The odes holdig the query with TTL= ad CTTL further forward the request to their eighborig clusters. If the CTTL expires, the query message is set to the server to locate the file holder. Each ode i the system reports files that are seldom queried by others to the server i order to guaratee the file availability. Such files are the files with visit rates lower tha a predefied threshold Ehaced itra-cluster routig algorithm I order to further improve the file search speed ad efficiecy, especially i a large-scale etwork, we ehace the radom-walk routig algorithm usig cotet based routig tables (CRTs). Table 1 shows a example for the CRT. I the table, the Cotet Idex refers to the hash value of a file explaied i Sectio 4.2.1, the Next Hop Node represets the ID of the eighbor ode that ca lead to the requested file with the Cotet Idex, ad the # Hops deotes the umber of remaiig hops to reach the requested file. TABLE 1 Cotet Based Routig Table Cotet Idex Next Hop Node # Hops I the ehaced routig algorithm, whe a ode receives a file request, it first checks whether there is a etry for the requested file i its CRT. If yes, the ode further checks whether the trust weight of the ext hop ode is larger tha a predefied threshold, which is high eough to determie that the ode is trustable. If yes, the request is forwarded to the ode. If there is o etry for the requested file or the ext hop ode is ot trustable, the origial radom-walk routig algorithm is used. We the itroduce the creatio ad maiteace of the CRTs. I the radom-walk routig, each request records the odes it has traversed i routig. Whe a request successfully locates the requested file, the recorded iformatio is forwarded back alog the origial routig path. Each ode i the path the updates its CRT. Specifically, it first checks whether there is a etry for the cotet idex of the requested file. If ot, the etry is added directly with the associated ext hop ad the umber of remaiig hops. Otherwise, it checks whether the ew route has fewer remaiig hops to reach the file. If yes, the etry is updated with the ew Next Hop Node ad # Hops. Figure 12 demostrates a example of the CRT update with the path iformatio of three successful requests. Whe a better routig path is discovered, the correspodig etry is updated. Note that such a CRT update will ot break the trust property of the routig protocol due to two reasos. First, the ew routig path is alog trustable odes as idicated i Sectio 4.2.1, so the ew path is trustable. Secod, whe usig the ext hop i a CRT, a ode eeds to make sure the ext hop is trustable. The path iformatio of a successful request is still forwarded back for odes to verify the activeess of the etries i its CRT. To cotiually discover better routes, each requester ca periodically use the radom walk algorithm. Cotet idex Next hop Node # Hops Origial CRT Received path iformatio Cotet idex Next hop Node # Hops Updated CRT Fig. 12. Update of the cotet based routig table. Whe a requester fails to fid its requested cotet followig the CRT, which meas the etries are outdated, it seds a otificatio to all odes o the path to delete the correspodig etries. Sice each cotet maitais at most oe etry i a CRT, the size of a CRT is bouded by the umber of cotets. I the case of limited memory resource, we ca limit the size of the CRT by deletig the etry with the oldest update time. As radom walk seds a query without directio, it may lead to a log path. Lettig all odes alog a log routig path record the ext hop would geerate a high overhead. The small world ature of social etworks idicates that two people ca be coected through social relatioships by six steps o average. Also, odes (c) 213 IEEE. Persoal use is permitted, but republicatio/redistributio 6 requires IEEE permissio. See

7 1.119/TPDS , IEEE Trasactios o Parallel ad Distributed Systems i a iterest cluster (i.e., commo-iterest-odes) should have more tight coectios. Thus, we expect that a ode i a cluster ca fid a file i aother ode i the same cluster withi o more tha 6 hops. Therefore, to reduce the CRT maiteace cost without compromisig the search efficiecy, we propose a advaced algorithm. It oly lets odes that are withi N h (N h 6) hops to the file holder update their CRTs for a successful request. This algorithm elimiates the uecessary overhead for updatig CRTs for log paths, ad also eables each ode to provide efficiet routig guidace for most files. As a result, CRTs ca lead the request to the file holder quickly. 4.3 Trust Relatioship Adjustmet Based o I3, cofies the query traffic to the socially close odes i order to make sure that the query ca be successfully forwarded ad the retrieved file is trustworthy. The trust relatioship adjustmet algorithm eables odes to avoid forwardig messages to malicious odes i order to reiforce the trustworthiess of the services i the system. Below, we use file provisio as a example for the service. Specifically, whe a file requester receives its queried file, if it fids that it receives a faulty file, the ode propagates a misbehavig ode otificatio back alog the previous query path. Each ode i i the routig path adjusts the weight of its lik to the ext hop o the path, so that it has lower probability of forwardig a message to the misbehavig ode. Sice a ode located closer to a misbehavig ode is more likely to forward a query to it, it eeds to reduce more weight o the lik to the misbehavig ode, ad vice versa. Also, the umber of hops betwee the queryig ode ad the misbehavig ode i the path affects the likelihood of the queryig ode to sed a query to the misbehavig ode. With these cosideratios, we desiged Equatio (3) for ode i i a path to adjust the weight of its lik to the ext hop j i the path: w(i, j) = w(i, j) α( b h ) h θ, (3) where b is the umber of hops from the queryig ode to ode i, h is the umber of hops betwee the queryig ode ad the misbehavig ode i the path, θ is a scalig parameter ad α is a weight parameter. Thus, the odes that are distat from the misbehavig odes (small b) reduce less lik weights, ad the odes that are closer to the misbehavig odes reduce more lik weights. Like previous reputatio systems [13], [15] [2], we set a threshold T h w which is a low weight value to idetify utrustworthy odes. If w(i, j) is less tha threshold T h w, ode i puts ode j ito the blacklist ad removes the P2P overlay lik to j. Sice it is possible that some faulty files are set out by some careless beig peers who did ot delete received faulty files from their sharig folder, periodically forgives the occasioal misbehavior of odes i the system i every T u time iterval by icreasig every ode s weight periodically: w(i, j) = Miimum{(w(i, j) + β), 1}, (4) where β > is the weight icrease value at every T u. T u is relatively very log ad β is very small. Therefore, it eeds a very log time for a ode to get a maximal reputatio 1 through the refreshig. Also, the weight decreasig speed of malicious odes is much faster TABLE 2 Parameter table Social etwork topology Facebook trace Number of iterests 18 Number of clusters 3 Chur rate Figure 5 CTTL ad TTL 3 ad 1 Lik weight threshold T h w.1 P2P ode degree threshold T h d 3 Lik weight update iterval T w 1s α, β, θ, T u.5,.1, 3, 1s tha this refreshig speed. Therefore, durig each time iterval, the variable w(i, j) still fuctios well. 5 PERFORMANCE EVALUATION We have coducted trace-drive experimets usig the trace data from Facebook ad BitTorret o Plaet- Sim [38] ad PlaetLab testbed [39]. We evaluated the efficiecy ad trustworthiess of the system i compariso with Partial Idexed Search () [14] ad [27]. is a hybrid system that clusters the odes based o their major iterests, ad also forms the odes ito a DHT to idex the o-major iterests ad globally upopular files for file retrieval. is a ustructured P2P system i which odes that share the same iterests are virtually clustered together if they have iteracted before. The odes use radom walk to locate iterest clusters ad to search for files. We also compare the file searchig trustworthiess performace of with Pure-P2P ad Eige- Trust [4]. Pure-P2P does ot have ay mechaisms to guaratee file trustworthiess. EigeTrust is a trust maagemet system, i which every peer has a trust maager to calculate its trust value based o others feedback. A ode s trust maager is the DHT ower of the ode s ID. Each ode seds the ratig of the file supplier to its trust maager after receivig a file. Table 2 lists partial parameters used i the experimets. Other parameters are derived from the trace data from Facebook ad BitTorret. We geerated 3, sythetic files accordig to the popularity distributio show i Figure 6, i.e., the umbers of files i each iterest follow the distributio i Figure 6. Each file is represeted by a ID ad a iterest. A file s ID is uique whe it is geerated, ad its iterest is selected followig the probability distributio derived from Figure 6, i.e., a file s probability to belog to iterest l is determied as the portio of torret for iterest l i Figure 6. Each file is assiged to a ode radomly selected from odes whose iterests match the file s iterest. The iterests of the odes ad the umber of odes were determied based o our Facebook trace. The file queryig rate is derived from the BitTorret trace. Figure 13 shows the average queryig rate of the odes i the BitTorret trace data alog with a lie for power-law distributio. We rak the odes i terms of the umber of queries issued by the odes. The ode geeratig the most queries is raked first. We see that the queryig rate of odes follows a power-law distributio. Thus, we used a power-law distributio geerator with scalig expoet parameter k=-1.2 to geerate a queryig rate withi the rage of [.1,1] messages per simulatio cycle (i.e., simulated secod), ad radomly assiged the rate to each ode i the system. Sice a ode is more likely to query files i its iterests [11], for each (c) 213 IEEE. Persoal use is permitted, but republicatio/redistributio 7 requires IEEE permissio. See

8 1.119/TPDS , IEEE Trasactios o Parallel ad Distributed Systems ode, 9% of its iitiated queries are for files i its ow iterests ad 1% are ot. The chur rate distributio of odes follows that of Figure 5. After a ode leaves the system, it waits for t w ad jois the system agai. t w is radomly selected from [1,1] simulatio cycles. Number of queries Number of queries Power law distribusio Rak of odes measured by issued queries Percet of traffic % Number of odes Fig. 13. Queryig rate. Fig. 14. Traffic distributio. We primarily use the followig six metrics for performace evaluatios: (1) Percet of traffic: the percet of query messages forwarded by differet kids of odes (frieds, ambassadors ad server) i a searchig stage. (2) Average query delay: the average delay of all file queries. (3) Query overhead: the total umber of query forwardig hops i file queryig of all file queries. (4) Maiteace overhead: the umber of messages i maitaiig the system structure i chur. (5) Overall overhead: the total umber of messages issued for file searchig, system maiteace ad trust maagemet. (6) Victimized probability: the percetage of odes receivig faulty files. 9% 8% 7% 6% 5% 4% 3% 2% 1% Itra Iter-1-hop Iter-2-hop Other We first coducted simulatio o PlaetSim (Sectio 5.1) ad the used Plaetlab for performace evaluatio i real sceario (Sectio 5.2). I both experimets, we disabled the advaced CRT-based routig algorithm proposed i Sectio We evaluate its performace separately i Sectio Performace Evaluatio o PlaetSim The umber of odes i the PlaetSim simulatio was set to 32,344, which is equal to the umber of odes i our crawled Facebook trace. The duratio of each experimet is 5, simulatio cycles. I each simulatio cycle, every ode i the system seds out oe query message. All experimets have bee coducted 1 times, ad the average values of the results are reported. I the figures, deotes i which a ode seds 1 message for a query, ad -c deotes i which a ode seds c messages for a file query i the radom walk file searchig i Evaluatio of File Sharig Efficiecy Traffic distributio. Figure 14 shows traffic distributio of the queries i versus etwork size. I this figure, Itra deotes the percetage of traffic i itra-clusterig searchig. Iter-1-hop ad Iter-2- hop deote the percetages of traffic i iter-cluster searchig whe the destiatio cluster is 1 ad 2 cluster hops away from the source cluster, respectively. The umber of cluster hops describes the distace betwee two clusters measured by the umber of clusters. A cluster is 1 cluster hop away from its eighborig cluster. The iter-cluster searchig traffic whe the destiatio is > 2 cluster hops away ad the traffic through the server are icluded i Other. The figure shows that about 7% of the queries ca be satisfied by the odes withi the same cluster. This implies that commomulti-iterests clusterig ca accurately cluster odes with similar multi-iterests. We also see that 99% of the queries ca be satisfied withi 2 cluster hops. This is because the odes i eighborig clusters also have similar multi-iterests. Therefore, these odes are very likely to satisfy the queries. This is the reaso that there is more traffic withi clusters 1 hop away tha clusters 2 hops away. The experimetal results also show that oly.1% of the traffic is through the server, which demostrates the effectiveess of iterest/trust-based radom walk ad P2P file sharig i. Figure 15 illustrates the distributio of the iter-cluster traffic through frieds, ambassadors ad the server, respectively. It shows that approximately 8% of the itercluster queries are set to the destiatio cluster through frieds, about 18% of the queries are forwarded through ambassadors, ad oly 1% are through the server. This result is cosistet with O6 that a ode has frieds i other clusters, which ca help the ode to forward its query to the other clusters. Sice a requester sometimes caot fid a fried i the destiatio cluster, it resorts to the ambassador for file searchig. Due to the TTL, sometimes a upopular file caot be discovered. This is the reaso that the server cotributes to a slight queryig traffic. Query delay. Figures 16 (a), (b) ad (c) show the query delay versus etwork size for queried files with three differet popularities, respectively. Popularity of a file is reflected by the percetage of the odes i the system holdig the file. Comparig the three figures, we see that as the popularity of the queried file decreases, the delay i ad icreases. Higher popularity files have more copies i the system, hece the probability that the files ca be retrieved from its eighbors is high, which results i a low query delay. The query delay i does ot icrease sigificatly whe file popularity decreases because for queryig upopular files, relies o the DHT, where the IDs of all odes holdig a file are stored together i oe ode. Thus, a upopular file ca always be located withi a limited umber of hops. However, the additioal DHT structure geerates high overhead for structure maiteace. I cotrast, for both ad which use radom walk for file retrieval, as file popularity decreases, the probability that a file is located ear the query ode decreases, thus the query delay icreases. The figures also show that the query delay i ad icrease sigificatly with etwork size while the query delay i icreases slightly. I, the odes iferred their iterests from files i their curret folders ad oly mai iterests are used. For ew types of files ad o-major iterest files, it uses the DHT for file retrieval. Sice the average trasmissio hops i the DHT icreases as etwork size icreases, query delay also icreases. I, the clusters are formed based o the iteractios betwee the odes. I a larger etwork, it takes loger time before the odes ca be clustered. Also, odes with the same iterests may be grouped to differet clusters because of the limited i (c) 213 IEEE. Persoal use is permitted, but republicatio/redistributio 8 requires IEEE permissio. See

9 1.119/TPDS , IEEE Trasactios o Parallel ad Distributed Systems Percet of traffic 1% 9% 8% 7% 6% 5% 4% 3% 2% 1% Fried Ambassador Server % Number of odes Average query delay (s) Number of odes Average query delay (s) Number of odes Average query delay (s) Number of odes (a) popularity=5% Fig. 15. Iter-cluster traffic distributio. Fig. 16. Delay versus etwork size. Query overhead Number of odes (a) popularity=5% Query overhead Number of odes (b) popularity=5% Fig. 17. Query overhead versus etwork size. teractio rage of odes. Thus, the isufficietly accurate clusterig i leads to loger query delay. Figure 16 (a) shows that for highly popular queried files, the query delay exhibits >>. oly clusters the odes based o their major iterests. However, popular files do ot ecessarily match the major iterests of the odes. Therefore, eeds to refer to the DHT for the file query. The O(log ) query hops lead to high query delay. Two factors cotribute to the higher delay of tha. First, the clusterig i is much more accurate tha. I, the odes are globally clustered based o their multi-iterest iformatio i their persoal profiles. The query ca always be satisfied withi the cluster with high probability without searchig other clusters. I, the clusterig is based o the iteractio history betwee the odes. Nodes with the same iterests may form several clusters because of the limited iteractio rage betwee them. The iaccurate clusterig leads to log query delay. Secod, has shorter iter-cluster query time. uses a stable DHT to locate a destiatio cluster. I cotrast, the cluster localizatio i is based o radom walk, which eeds more query time. I Figure 16 (b), we see that for queried files with media popularity, the query delay of icreases rapidly ad exceeds DIS whe the umber of odes is 3,. This is because the lower popularity of a file leads to a loger time for ode clusterig ad itra-cluster search i, ad large etwork size exacerbates the delay due to radom walk. ca accurately cluster the odes with similar iterests. Therefore its overall delay is lower tha. However, as show i Figure 16 (c), whe file popularity is very low, the file search delay of radom walk i is log. has a short delay i a small-size etwork due to the small size of DHT. As a result, geerates higher delay tha. leads to higher delay tha others because low popularity of the queried file leads to a loger time for ode clusterig ad itra-cluster search. Figure 16 shows that -3 ad -5 have the smallest trasmissio delay with differet Query overhead (b) popularity=5% Number of odes (c) popularity=.5% Maiteace overhead (c) popularity=.5% Number of odes Fig. 18. System maiteace overhead. popularities of the queried files. This is because sedig out more copies of the query messages ca icrease the hit probability i. Therefore, for upopular files i the system, ca reduce the query delay by sedig more query copies. Query overhead. Figures 17(a), (b) ad (c) show the query overhead versus the etwork size for queryig files with three popularities. The figures show that as the etwork size icreases, the amout of system overhead icreases, which is the outcome of the icreased average query hops i the etwork. Figure 17 (a) shows that the query overhead of is larger tha all other systems for queryig files with high popularity. Due to the high popularity of the queried files, other systems ca fid the files i the eighbor odes with high probability. However, DHT routig i leads to high routig overhead. I, sice the odes are ot well clustered iitially, it takes more hops to fid a file tha which are well clustered. -3 ad -5 produce higher query overhead tha because of more messages. Because every copy of the query message ca be satisfied withi a small umber of hops, the overall overheads of -3 ad -5 are less tha. As show i Figures 17 (b) ad (c), for the files with lower popularity, the average query overhead i Social- P2P ad icreases sharply, because the radom walk algorithm takes more hops to meet lower popularity files. For -c, as there are c idividual copies set out for file retrieval, the overhead icreases extremely fast. The query overhead i exhibits a very slight icrease as popularity decreases because it largely depeds o the DHT. The routig overhead i DHT icreases over the file popularity due to the same reaso as i Figure 16. We also ote that the query overhead of -c is ot c times of that of. This is because Social- P2P-c oly has multiple messages durig the radom walk process, while the query overhead refers to the total umber of query forwardig hops. Sice the radom walk oly take few hops, the overall query overhead does ot icrease liearly i -c. All these ex (c) 213 IEEE. Persoal use is permitted, but republicatio/redistributio 9 requires IEEE permissio. See

10 1.119/TPDS , IEEE Trasactios o Parallel ad Distributed Systems Overall overhead Social- P2P Social- P2P-3 Social- P2P-5 Fig. 19. System overall overhead. Victimize probability 5.% 4.% 3.% 2.% 1.%.% Pure-P2P EigeTrust -NA Simulatio time Fig. 2. Victimized probability with malicious odes. perimetal results i Figure 17 verify the low overhead of i file queryig. Maiteace overhead. Figure 18 shows the system maiteace overhead of, ad versus the etwork size. It shows that has the highest system maiteace overhead ad it icreases sharply as etwork size icreases. Its overhead is maily caused by the DHT structure maiteace, which leads to a high overhead especially i chur. costructs ambassadors ito a DHT for iter-cluster commuicatio. Sice the size of the DHT is small, the ambassadors are relatively stable, ad the other odes oly eed to maitai their coectio with their frieds, oly produces a slight maiteace overhead. is also a ustructured P2P etwork. Sice each ode must maitai several iterest clusters, geerates higher maiteace overhead tha ad its maiteace overhead icreases rapidly as the etwork size grows. Overall system overhead. Figure 19 shows the overall system overhead of, ad i a etwork with 32,344 odes. The figure shows that has the highest overall system overhead. Although the query overhead of for upopular files is small, the DHT maiteace overhead i is very large, which leads to a extremely high overhead. Sice has a lower query ad system maiteace overhead, its overall overhead is the lowest. Although the icreasig umber of query copies lead to a higher overall system overhead i, it is still less tha. cosumes a high overhead for cluster formatio ad multi-cluster maiteace. Therefore, has the secod highest overall system overhead Evaluatio of File Sharig Trustworthiess I this sectio, we evaluated the queryig trustworthiess of i compariso with Pure-P2P ad EigeTrust. is evaluated with two mechaisms: (1) The routig is ot aoymous. Malicious odes do ot sed faulty files to their socially close odes, but they sed faulty files to the requesters 3 hops away i the social etwork. This mechaism is deoted as -NA. (2) The routig is aoymous. Malicious odes are ucostraied ad reply to every query with a faulty file. This mechaism is deoted as Social- P2P. I order to make the methods comparable, all systems use the commo-multi-iterest clusterig mechaism for file sharig. I the experimets, 5 odes out of the 32,344 odes were radomly selected to act as malicious odes. A file requester that has received a faulty file will ot forward the file to other odes. Performace uder malicious odes. Figure 2 shows the victimized probability over the simulatio time. It Victimize probability 5.% 4.% 3.% 2.% 1.%.% Pure-P2P EigeTrust -NA Simulatio time Fig. 21. Victimized probability uder collusio. Overall overhead Pure-P2P EigeTrust -NA Number of odes Fig. 22. System overall overhead. shows that i Pure-P2P, without ay protectio, a large percetage of odes costatly receive faulty files. I EigeTrust, iitially the victimized probability is very large ad the gradually decreases to. This is because the odes i EigeTrust do ot have ay reputatio iitially, which leads to a high victimized probability. However, as EigeTrust decreases the reputatio of the malicious odes, other odes o loger query files from malicious odes due to their low reputatios. Therefore, the victimized probability of EigeTrust decreases. I -NA, the probability of the odes receivig faulty files is the lowest iitially, because a very small umber of queries from socially distat odes ca be received by the malicious odes. Sice trust relatioship adjustmet ca further reduce the probability of forwardig query messages to the malicious odes, its victimized probability decreases. I, because malicious odes sed faulty files to all requesters, the victimized probability is iitially high. Sice the eighbors of the malicious odes ca quickly stop forwardig messages to the malicious odes, the victimized probability decreases sharply. The results imply that if the malicious odes do ot sed faulty files to their frieds i order to avoid degradig their reputatios i real life, ca provide higher file sharig trustworthiess tha EigeTrust. Eve if all malicious odes are ucostraied ad sed faulty files to all odes i the system, the performace of is still comparable to EigeTrust. Performace uder malicious ad colludig odes. Malicious odes may collude to ehace the reputatios of each other by ratig each other highly. I this experimet, we let 1 out of the previous 5 malicious odes to act as colludig odes. Figure 21 shows that Pure-P2P still suffers from high victimized probability over time due to the same reaso i Figure 2. For EigeTrust, sice the malicious odes collude with each other to ehace their reputatios, the queries are still forwarded to the colluders. Sice odes geerate differet amouts of queries over time, the victimized probability of odes fluctuates over time. We ca see that EigeTrust produces much higher victimized probability tha ad -NA. For both ad -NA system, the colludig group ca be idetified withi 1,s. The reaso is that although the weights betwee the liks amog the colluders are ot reduced, the weights of the liks to the colluders are reduced. Therefore, i a short period of time, the etire colludig group is isolated. Sice i -NA, oly the socially distat odes from malicious odes receive faulty files, the victimized probability of -NA is much less tha (c) 213 IEEE. Persoal use is permitted, but 1 republicatio/redistributio requires IEEE permissio. See

11 1.119/TPDS , IEEE Trasactios o Parallel ad Distributed Systems Percet of traffic 9% 8% 7% 6% 5% 4% 3% 2% 1% % Fried Ambassador Server Fig. 25. Iter-cluster traffic distributio. Average query delay (s) % 1% 2% 3% 4% 5% Popularity Fig. 26. Query delay versus popularity. Overall overhead. Figure 22 compares the overall overhead of the Pure-P2P,, -NA ad EigeTrust. The figure shows that the overheads of all systems icrease as the etwork size icreases sice they eed to maitai more odes ad queries are routed i a larger scale. The figure shows that Pure- P2P has the lowest overhead because it has o reputatio maagemet. EigeTrust icurs more overhead tha ad Social-NA because it has doubled overhead due to file sharig ad trust maagemet. The DHT maiteace ad reputatio maagemet system leads to a high overhead. The odes i ad Social-NA oly eed to locally adjust their lik trust weights to their eighbor odes whe receivig misbehavior otificatio messages. Therefore, the overhead i is extremely small ad is close to Pure- P2P. Sice ad -NA have the same trust maagemet ad routig mechaisms, their overall overheads are the same. The experimetal result verify the advatages of dealig with efficiet ad trustworthy file sharig simultaeously, ad the low overhead of lik trust weight adjustmet. Server overhead. We measured the overhead for a server as the umber of requests it hadles plus the umber of recommedatios it makes. Queryig frequecy meas the probability that a ode geerates a query i the roud of simulatio. We varied the queryig frequecy from.4 to.8 i Server overhead (x1 5 ) Queryig frequecy=.4 Queryig frequecy =.6 Queryig frequecy = Number of odes Fig. 24. Server overhead. the experimets. Figure 24 shows the server overload versus the umber of odes ad the queryig frequecy. We see that the server overhead icreases almost proportioally with the queryig frequecy. This is because whe the queryig frequecy icreases, more queries are geerated, leadig to more overhead to the server. We also see that the server overhead icreases as the umber of odes icreases, sice more odes lead to more recommedatios from the server ad more queries that eed to be hadled by the server. 5.2 Performace Evaluatio o PlaetLab We implemeted a prototype of SocialP2P o PlaetLab i order to show the performace of SocialP2P i the real world eviromet. We chose 3 olie odes i PlaetLab ad chose the computer with the IP address at Priceto Uiversity as the server. Sice the coectio ad badwidth betwee the odes vary over time, we ra the cliet program o each PlaetLab ode twice at differet times. The average results of each Query overhead % 1% 2% 3% 4% 5% Popularity Fig. 27. Query overhead Fig. 28. System overall overhead. versus popularity. experimet are preseted i the paper. We radomly selected 3 odes from the trace data ad mapped the odes to the PlaetLab odes. We geerated 3 sythetic files accordig to the popularity distributio show i Figure 6 ad radomly distributed these files to the odes whose iterests match the file cotets. The duratio of each experimet was set to 5s. Overall overhead % 5% 1% 2% 3% 4% 5% Popularity Evaluatio of File Sharig Efficiecy Figure 25 shows the percet of traffic through frieds, ambassadors ad servers. We see that almost 8% of the traffic goes through frieds, 19% of the traffic goes through ambassadors ad oly 1% through the server. The result is cosistet with Figure 15 due to the same reasos. Figure 26 plots the average query delays of odes versus file popularity. The figure shows that as the file popularity icreases, the average query delay of ad decreases while the average query delay of remais almost the same. The results of relative performace betwee differet systems are cosistet with Figure 16 due to the same reasos. We also fid that the absolute results o PlaetLab are larger tha those i simulatio i Figure 16 sice a message eeds a loger latecy to travel betwee two odes i the PlaetLab real-world testbed. Figure 27 shows the query overhead of, ad versus file popularity. We see that the query overhead of is almost costat ad those of ad decrease as the file popularity icreases. These results are cosistet with those i Figure 17 due to the same reasos. We also fid that the absolute results o PlaetLab are smaller tha those i simulatio i Figure 17 because the PlaetLab experimet has a much smaller etwork scale. Figure 28 plots the overall overhead of the systems versus ode popularity. We see that has much higher overall overhead tha ad. These results are cosistet with the simulatio results i Figure 19 due to the same reasos. The lower absolute overall overhead i the PlaetLab experimet is caused by the smaller scale etwork i the test. Victimized probability 4.5% 4.% 3.5% 3.% 2.5% 2.% 1.5% 1.%.5%.% Pure-P2P EigeTrust -NA Time (s) Fig. 29. Victimized probability with malicious odes. Victimized probability 4.5% 4.% 3.5% 3.% 2.5% 2.% 1.5% 1.%.5%.% Pure-P2P EigeTrust -NA Time (s) Fig. 3. Victimized probability uder collusio (c) 213 IEEE. Persoal use is permitted, but 11 republicatio/redistributio requires IEEE permissio. See

12 1.119/TPDS , IEEE Trasactios o Parallel ad Distributed Systems Evaluatio of File Trustworthiess We evaluated the queryig trustworthiess of Social- P2P i compariso with Pure-P2P ad EigeTrust o PlaetLab. We radomly selected 15 odes out of 3 odes as malicious odes. We evaluated i two scearios: (1) malicious odes are ot colluders, ad (2) malicious odes are colluders. A ode that has received a faulty file will ot forward the file to others. Figure 29 shows the victimized probability of odes i the system over time. We see that Pure-P2P leads to a high victimized probability over time. I cotrast, the victimized probability of the odes i -NA, ad EigeTrust decreases over time. Social- P2P-NA has a much smaller victimized probability tha EigeTrust ad. ca still reach the comparable performace as EigeTrust. These relative results are cosistet with Figure 2 due to the same reasos. Figure 3 shows the victimized probability of odes i differet systems over time whe the malicious odes are colluders. Similar to Figure 29, the victimized probability of odes i Pure-P2P is much higher tha all other systems. -NA, ad EigeTrust ca gradually detect the malicious odes by trust maagemet. The figure also shows that the victimized probability of EigeTrust is always larger tha. For both ad -NA, the colludig malicious odes ca be effectively detected. The victimized probability of -NA is much less tha. These results are cosistet with Figure 21 due to the same reasos. We otice that the absolute results of the victimized probability i the PlaetLab experimets are lower tha those i simulatio because the PlaetLab experimets have fewer malicious odes tha the simulatio. Figure 31 plots the system overall overhead of differet systems. The overall overhead i EigeTrust is the highest. The overall overhead i Pure-P2P is the smallest. The system overall overhead i ad -NA are comparable to Pure-P2P. We also see that Social- Overall overhead Pure-P2P EigeTrust -NA Fig. 31. System overhead. P2P-NA produces smaller overhead tha. These relative results are cosistet with Figure 22 due to the same reasos. We fid that the PlaetLab experimet has higher absolute results tha simulatio because of its smaller scale of the etwork. 5.3 Evaluatio of the CRT-based Ehaced Routig Algorithm We further evaluate the CRT-based ehaced routig algorithm proposed i Sectio Recall that we proposed both a basic method ad a advaced method. For the basic method, we tested two variaces, amely ad. They are differet o the start time whe the cotet based routig table ca guide file searchig. I detail, the cotet based routig table i ad starts to guide file searchig sice the begiig of the experimet ad sice half of the experimet, respectively. We also tested the performace of Social- P2P without the CRT-based ehaced routig algorithm ad with the advaced method, which are deoted as ad Advaced-CRT, respectively. I this test, sice memory is ot a bottleeck o moder computers, we did ot limit the size of the CRT o each ode. Cosiderig the social etwork used i our experimet is more tightly coected (i.e., from Facebook), we set N h = 3 i the Advaced-CRT, which guides the file searchig sice the begiig of the experimet. I order to maximally ad directly show the ifluece of differet variaces, we disabled the fuctio of periodically usig radom walk to discover better routes metioed i Sectio We focused o the simulatio o PlaetSim for performace evaluatio but also provided some results from the PlaetLab experimet. I the simulatio, we followed the same default setup of cluster structure ad file distributio as i the settig i Sectio 5.1. I the PlaetLab experimet, we followed the settigs i Sectio 5.2. The results of simulatio ad PlaetLab experimet are show i Sectio ad Sectio 5.3.2, respectively. I both experimets, sice the experimetal results become stable after tes of secods of ruig, we oly show the results of 1 cycles i the simulatio ad 8s i the PlaetLab experimet below Results from PlaetSim Simulatio Routig Hops: We measured the umber of routig hops from a file requester to the file holder for each successful request. Figure 32(a) ad 32(b) show the total umber ad average umber of routig hops per request of successful requests i each cycle i the experimets, respectively. We see that has the most query hops i every secod. reduces the query hops ad produces the secod most query hops. geerates the same umber of query hops as i the first half of experimet ad the lowest umber of query hops i the secod half of experimet. Advaced-CRT reduces the umber of query hops gradually to a very low level. I, requests are forwarded blidly, leadig to the most query hops. reduces the hops of due to guidace of CRTs. However, sice it uses the first sets of routes discovered through radom walk to guide subsequet requests, it caot discover better routes. Thus, oly slightly decreases the umber of query hops of. I, the CRTs are well built i the first 5 secods, so they ca guide route requests to file holders effectively, leadig to the lowest umber of hops. I Advaced-CRT, routes are oly built o earby odes of file holders. Therefore, odes gradually lear the earby cotets to guide requests, ad the umber of query hops reduces gradually to a very low level. This result idicates that a small N h ca esure the file search efficiecy. Percetage of Routig Hops i Local/Foreig Clusters: Recall that if a request caot fid the requested file i a local cluster, it searches i the foreig clusters. We measured the percetage of routig hops i local clusters ad foreig clusters of successful requests. The results are show i Figure 32(c) ad Figure 32(d). Figure 32(c) shows that has the lowest percetage of (c) 213 IEEE. Persoal use is permitted, but 12 republicatio/redistributio requires IEEE permissio. See

13 1.119/TPDS , IEEE Trasactios o Parallel ad Distributed Systems Total # of hops (x1 5 ) RadomWalk Simulatio cycle (a) Total umber of routig hops. Average # of hops Simulatio cycle (b) Average umber of routig hops. % of requests solved i local clusters RadomWalk Simulatio cycle % of requests solved i foreig clusters (c) Percetage of hops i local clusters. (d) Percetage of hops i foreig clusters. Fig. 32. Performace of differet CRT-based ehaced routig algorithms i PlaetSim simulatio. routig hops i local clusters ad has the secod highest percetage. has the same percetage as i the first half of experimet ad high percetage i the secod half of experimet. Advaced- CRT icreases the percetage gradually to the highest level. Figure 32(d) exhibits the opposite treds sice the sum of the two percetages equals 1. has a low percetage of hops i local clusters because requests are always forwarded through the radom walk, which has a high probability to fail i the local cluster ad resort to the eighborig clusters. For, it is the same as i the first half of experimet as it does ot use CRTs i file searchig. I the secod half of experimet, the well-built CRTs lead requests to file holders i the same cluster quickly, leadig to a higher percetage i local clusters. I ad Advaced-CRT, their CRTs provide file locatios withi local clusters, leadig to a high percetage of hops i local clusters. Costs of the Cotet Based Routig Tables: Figure 33(a) ad 33(b) demostrate the average size (i.e., total umber of etries of all CRTs divided by the total umber of odes) ad the maiteace cost (i.e., the umber of routig path iformatio forwards) of the CRTs i each cycle, respectively. We also iclude i the figures for referece though it has o cost o CRTs. Figure 33(a) shows that the average size of CRTs follow >>Advaced-CRT. produces the largest average CRT size due to two reasos. First, it builds CRTs i all odes alog the full path of each successful request. Secod, it builds CRTs durig the first half of experimet. I the secod half of test, requests just follow the established CRTs ad do ot discover ew routes for existig routes, leadig to early stable average size of CRTs. I, oce a etire route from a ode to a file holder is built alog odes i the path, o ew routes for the queried file will be discovered. Thus, produces smaller average CRT size tha. average size icreases gradually as routes for ew files are added. For Advaced-CRT, each ode oly eeds to record the eighbors for the earby cotets ad o ew routes for queried files will be discovered, leadig to the smallest CRT size. Figure 33(b) shows that i each cycle, always geerates a high maiteace cost, has the highest maiteace cost i the first half of the experimet ad a low maiteace cost thereafter, ad Advaced-CRT produces the lowest maiteace cost. has high maiteace cost because the path iformatio of each successful request is set to all odes o the path to update the routes. I, the whole path iformatio is forwarded alog all odes i a path i the first half of the test, leadig to a high maiteace cost. I the Average CRT size RadomWalk Simulatio cycle CRT mait. cost (x1 5 ) RadomWalk Simulatio cycle (a) Average size of the CRT tables. (b) Maiteace cost of the CRT tables Simulatio cycle Fig. 33. Costs of differet CRT-based ehaced routig algorithms i PlaetSim simulatio. Average # of hops Simulatio time (s) (a) Average umber of routig hops. Average CRT size RadomWalk Simulatio time (s) (b) Average size of the CRT tables Fig. 34. Performace of differet CRT-based ehaced routig algorithms i PlaetLab experimet. secod half of the test, the CRTs are used i file searchig ad path iformatio is forwarded alog odes i a shorter path, reducig the maiteace cost to a low level. Advaced-CRT limits the umber of odes to receive the path iformatio to N h = 3, geeratig the lowest maiteace cost. With the above results, we coclude that Advaced-CRT leads to search performace improvemet comparable to CRT but at the lowest cost Results from Plaetlab Experimet Figures 34(a) ad 34(b) show the average umber of routig hops ad average size of the CRT tables i the PlaetLab experimet. Note that results o other metrics are ot show here due to page limit ad the fact that they show similar treds as the two metrics.we see that the relatioship betwee the performace of four methods is cosistet with those i PlaetSim Simulatio, i.e., Figures 32(b) ad 33(a). The reasos are also the same. Such results demostrate the superiority of the advaced-crt algorithm. We also see that the four methods have smaller average routig hops ad average CRT table size i the PlaetLab experimet tha i the PlaetSim simulatio. This is because the PlaetLab experimet is o a small scale with oly 3 odes. This further shows the scalability of the proposed CRT based routig algorithm. 6 CONCLUSION I this paper, drive by the observatios from the trace data of Facebook ad Bittorret file sharig system, we (c) 213 IEEE. Persoal use is permitted, but 13 republicatio/redistributio requires IEEE permissio. See

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Estri, ad L. the Yi, Dataad[15]theS. Ratasamy, prototypeb. o PlaetLab demostrate effi- [29] D. Frey, A. Je gou, A.-M. Kermarrec, M. Rayal, ad J. Staier. storage i sesorets with GHT, a geographic hash table, Trust-aware peer samplig: Performace ad privacy tradeoffs. ciecycetric ad trustworthiess of file sharig i Haiyig She received the BS degree i ComMONET, vol. 8, pp , 23. Theoretical Computer Sciece, 213. i compariso other file sharig systems rage ad queries trust [3] Bittorret user activity puter traces. Sciecehttp:// pavlo/ ad Egieerig from Togji Ui[16] X. Li, Y. J. with Kim, ad W. Hog, Multi-dimesioal Chia i 2, ad the MS ad Ph.D. torret/ [accessedversity, i Aug. 214]. i sesor etworks, maagemet systems. iiproc. ourof SeSys, future23. work, we will exdegrees frommodel Wayefor ad i C. Computer S. Yag. Egieerig A vector space [17]the D. Iformatio Gaesa, DIMENSIONS: Why do we eed ew data [31] G. Salto, A. Wog, ploit Cetric Networkig (ICN)a routig automatic idexig. Commuicatios of the ACM, State Uiversity i 24 ad 26,1975. respectively. hadlig architecture for sesor etworks, i Proc. of the ACM to improve file performace. [32] D. Bayer ad M. Stillma. Computatio of Hilbert fuctios. JSC, She is curretly a Assistat Professor i the HotNets,the 22, pp.searchig Departmet of Electrical ad Computer Egi[18] D. Gaesa, A. Cerpa, Y. Yu, D. Estri, W. Ye, ad J. Zhao, [33] H. She, Y. Li, eerig, ad T. Li. Combiig fidelity ad the Director ofefficiecy, the Pervasive Com-ad Networkig issues i wireless sesor etworks, JPDC, 24. ACKNOWLEDGEMENTS flexibility i resource iformatio services. TC, 213.Uiversity. Laboratory of Clemso [19] B. Greestei, D. Estri, R. Govida, S. Ratasamy, ad [34] H. She ad K.muicatios Hwag. clusterig This research supported i idex part for byfeatures U.S. NSF grats Her researchlocality-preservig iterests iclude distributed com-ad S. Sheker,was Difs: A distributed i sesor etdiscovery of resources i wide-area distributed computatioal puter systems ad computer etworks, with a NSF , CNS-12546, CNS , works, i IIS , Proc. of SNPA, 23. grids.otc, 212. emphasis peer-to-peer ad cotet delivery etworks, mobile com[2] J. Li, J. Jaotti, D. S. J. De, C. David, R.Faculty Karger, adfellowship R. Morris, A [35] CNS-91756, Microsoft Research P. Godfrey, S. Sheker, ad I. Stoica. Miimizig chur i wireless sesor etworks, ad grid computig. Her research scalable locatio service for geographic ad hoc routig, i Proc. of putig, distributed systems. I Proc. of Sigcomm, work G. has bee published top jourals cofereces these MobiCom, 2. [36] Swamyatha, C. i Wilso, B. Boe,ad K. C. Almeroth,iad B. Y. She was Program Co-Chair for a e-commerce: umber of iteratioal Zhao. Ca the Social Networks Improve a Study o [21] J. Newsome ad D. Sog, GEM: Graph EMbeddig for routig areas. R EFERENCES cofereces ad member ofithe Program Committees Social Marketplaces. Proc. of WOSN, 28. of may leadig ad data-cetric storage i sesor etworks without geographic cofereces. is ad.member of the Social IEEE ad ACM. She is Microsoft [37] C. BizelShe ad Fehr. How Distace Affects Trust ad [1] P2P Statistics. i Proc. of SeSys, 23. Faculty Fellow of 21. Evidece from A Slum. I Proc. of Cooperatio: Experimetal statistics.html 214]. [22] A. Caruso, [accessed S. Chessa,iS.Aug. De, ad R. Urpi, GPS free coordiate Research ERF, 29. [2] I. Stoica, R. Morris, D. Karger, M. F. Kaashoek, ad H. Balakrishassigmet ad routig i wireless sesor etworks, i Proc. of [38] P. Garca, C. Pairot, R. Mode jar, J. Pujol, H. Tejedor, ad R. Rallo. a. Chord: A scalable IEEE INFOCOM, 25,peer-to-peer pp lookup protocol for Iteret PlaetSim: a ew overlay etwork simulatio framework. I Proc. applicatios. TON,D.23. [23] P. Desoyers, Gaesa, ad P. Sheoy, Tsar: A two tier of SEM, 24. [3] S. sesor Ratasamy, P. Fracis, M. usig Hadley, R. Karp, S. Sheker. storage architecture iterval skip ad graphs, i Proc. [39] L. Peterso, A. Bavier, M. Fiuczyski, ad S. Muir. Experieces A of scalable cotet-addressable etwork. I Proc. of SIGCOMM, SeSys5. ACM Press, 25, pp implemetig PlaetLab. I Proc. of OSDI, C. T. Ee adad S. Ratasamy, Practical data-cetric i Proc. [4] S. Kamvar, M. Schlosser, ad H. Garcia-Molia. The EigeTrust [4] [24] A. Rowstro P. Druschel. Pastry: Scalable,storage, decetralized Liayu Zhao received the BS ad MS degrees of NSDI, 26. algorithm for reputatio maagemet i P2P etworks.chia. I Proc. object locatio ad routig for large-scale peer-to-peer systems. i Computer Sciece from Jili Uiversity, [25] M. Aly, K. Pruhs, ad P. K. Chrysathis, KDDCS: A loadof WWW, 23. He is curretly a Ph.D. studet i the DepartI Proc. of Middleware, 21. Haiyig She received the BS degree i Combalaced i-etwork data-cetric storage scheme i sesor et[5] Y. Liu, X. Liu, L. Xiao, L.M. Ni, ad X. Zhag. Locatio-aware met of Electrical ad Computer Egieerig puter Sciece ad Egieerig from Togji Uiwork, matchig i Proc. of i CIKM, pp topology P2P 26, systems. I Proc. of Ifocom, 24. of Clemso Uiversity. His research iterests versity, Chia i 2, ad the MS ad Ph.D. F. Bia, X. Li,ad R. Govida, S. Scheker, Usig hierarchical [6] [26] Y. Liu, L. Xiao, L. M. Ni. ad Buildig a scalable bipartite P2P iclude sesor Egieerig etwork, routig degreeswireless i Computer fromprotowaye locatio ames TPDS, for scalable overlay etwork. 27. routig ad redezvous i wireless cols, ad security issuesrespectivei P2P Stateapplicatios Uiversity i 24 ad 26, [7] H. sesor Xie ad Y. R. Yag. P4P:ofProvider portalpp. for applicatios. I etworks, i Proc. SeSys, 24, etworks. ly. She is curretly a Associate Professor i Proc.J. ofxu, ACM Sigcomm, [27] X. Tag, ad 28. W. chie Lee, A ew storage scheme for the Departmet of Electrical ad Computer E[8] D. approximate R. Choffeslocatio ad F. queries E. Bustamate. Tamigsesor the torret: A i object trackig etworks, gieerig at Clemso Uiversity. Her research practical approach to pp. reducig cross-isp IEEE TPDS, vol. 19, , 28. traffic i P2P systems. iterests iclude distributed computer systemi Proc. of Sigcomm, 28. [28] M. Li ad Y. Liu, Redered path: rage-free localizatio i s ad computer etworks, with a emphasis [9] A. aisotropic Iamitchi, sesor M. Ripeau, adwith I. Foster. file-sharig etworks holes, Small-world i Proc. of MobiCom, 27. o P2P ad cotet delivery etworks, mobile commuities. I Proc. Ifocom, 24. [29] H. She, T. Li, ad T.ofSchweiger, A Efficiet Similarity Searchig computig, Haiyig She wireless received the BS degree i Computer ad Egieerig from sesor etworks, ad Sciece cloud computig. She is Togji a [1] J. Lei ad X. Fu. Iterest-based peer-to-peer group maagemet. Scheme Based o Locality Sesitive Hashig, i Proc. of ICDT, Microsoft Uiversity, Faculty Chia i Fellow 2, adofthe MS ad Ph.D. degrees i Computer Egieerig 21, a seior member of the IEEE ad afrom Lecture otes i computer sciece, 29. Waye State Uiversity i 24 ad 26, respectively. She is curretly a Assistat 28. member of the ACM. [11] A. Fast, D. Jese, ad B. N. Levie. Creatig social etworks to Electrical adicomputer Egieerig ZeDepartmet Lireceived receivedof the BSdegree degree i Electroics [3] D. Karger, E. peer Lehma, T. Leighto, M. of Levie, Lewi, ad Professor i the Holcombe Ze Li the BS Electroics adad at improve peer to etworkig. I Proc. KDD, D. 25. Iformatio Egieerig from Huazhog UiverClemso Uiversity. Her Iformatio research iterests iclude distributed ad parallel computer R. Paigrahy, Cosistet Hashig Radom Trees: Distributed Egieerig from Huazhog Uiver[12] Y. Li, L. Shou, ad K. L. Ta. Cyber:ad A commuity-based search sityofofwith Sciece adtechology, Techology, Chia i 27, a emphasis o peer-to-peer adicotet delivery Cachig Protocols for28. Relievig Hot Spots o the World Wide systems ad computer etworks, sity Sciece ad Chia, 27. egie. I Proc. of P2P, adwireless Ph.D. Computer Egieerig etworks, mobile computig, sesordegree ad grid cloud computig. Web, P. i Gaesa, Proc. of STOC, 1997, pp He isthe curretly a etworks, Ph.D. istudet i ad the Depart[13] S. Marti, ad H. G. Molia. DHT routig usig social fromforclemso Uiversity. Hiscofereces research iterests of iteratioal ad member of [31] W. I Nejdl, Siberski,24. M. Wolpers, ad C. Schmitz, Routig She was the Program Co-Chair liks. Proc.W. of IPTPS, met ofa umber Electrical ad Computer Egieerig iclude distributed etworks, aiterests emphasis may leadig cofereces. She is awith Microsoft Faculty Fellow clusterig i Hu. schema-based super peer etworks, i partial Proc. of the Program Committees ofof [14] R. ad Zhag ad Y. C. Assisted peer-to-peer search with Clemso Uiversity. His research oieee peer-to-peer cotet delivery etworks. of 21 ad a member of the ad ACM. ad IPTPS, 23. idexig. TPDS, 27. iclude distributed etworks, with a emphasis He is curretly a data scietist i the MicroStrat[15] [32] L. Xiog ad L. Liu. reputatio-based trust P. A. Berstei, F. PeerTrust: Giuchiglia,supportig A. Kemetsietsidis, J. Mylopoulos, o peer-to-peer ad cotet delivery etworks, egy Icorporatio. forl.peer-to-peer commuities. TKDE, 24. Serafii, adelectroic I. Zaihrayeu, Data maagemet for peer-to-peer wireless multi-hop cellular etworks, game the[16] R. computig: Zhou ad AK.visio, Hwag. PowerTrust: robust ad scalable i Proc. of WebDB,A22. ory adche data miig. Hethe is BS a studet of Kag received degreemember i Electroreputatio system trusted peer-to-peer computig. TPDS. [33] A. Y. Halevy, Z. for G. Ives, P. Mork, ad I. Tatariov, Piazza: Data IEEE. ics ad Iformatio Egieerig from Huazhog 27. maagemet ifrastructure for sematic web applicatios, i Proc. Uiversity of Sciece ad Techology, Chia i [17] R. of Zhou, K. 23. Huag, ad M. Cai. GossipTrust for fast reputatio WWW, 25, the MS i Commuicatio ad Iformatio aggregatio i peer-to-peer etworks. TKDE, 28. Systems from the Graduate Uiversity of Chi[18] S. Sog, K. Hwag, R. Zhou, ad K. Y. Kwok. Trusted P2P trasese Academy of Scieces, Chia i 28, ad actios with fuzzy reputatio aggregatio. Iteret Computig, the PhD i Computer Egieerig from Clemso 25. Uiversity. He is curretly a Postdoctoral Fellow [19] Z. Liag ad W. Shi. PET: a persoalized trust model with i the Departmet of ECE at Clemso Uiverreputatio ad risk evaluatio for P2P resource sharig. I Proc. sity. His research iterests iclude vehicular etof HICSS, 25. works, software defied etworks, mobile ad hoc [2] A. Selcuk, E. Uzu, ad M. R. Pariete. A reputatio-based trust etworks ad delay tolerat etworks. maagemet system for P2P etworks. IJNS, 28. Cheg-Zhog Xu received B.S. ad M.S. degrees from Najig Uiversity i 1986 ad 1989, respectively, ad a Ph.D. degree i Computer Sciece from the Uiversity of Hog (c) 213 IEEE. Persoal use is permitted, but 14 republicatio/redistributio requires IEEEipermissio. See of Electrical ad Computer Kog i He is curretly a Professor the Departmet foruiversity more iformatio. Egieerig of Waye State ad the Director of Su s Ceter of Excellece i