R 5 N: Randomized Recursive Routing for Restricted-Route Networks

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1 R 5 N: Radomized Recursive Routig for Restricted-Route Networks Natha S. Evas Techische Uiversität Müche Muich, Germay evas@et.i.tum.de Christia Grothoff Techische Uiversität Müche Muich, Germay grothoff@et.i.tum.de Abstract This paper describes a ew secure DHT routig algorithm for ope, decetralized P2P etworks operatig i a restricted-route eviromet with malicious participats. We have implemeted our routig algorithm ad have evaluated its performace uder various topologies ad i the presece of malicious peers. For small-world topologies, our algorithm provides sigificatly better performace whe compared to existig methods. I. INTRODUCTION Distributed Hash Tables (DHTs) [13], [11] are a key data structure for the costructio of completely decetralized applicatios. DHTs are importat because they geerally provide a robust ad efficiet meas to distribute the storage ad retrieval of key-value pairs. I recet years, DHT desigs have become icreasigly efficiet ad robust uder chur [9], [12], [14], [18] ad Sybil attacks [1], [16], [19]. Other research has addressed implemetatio cocers, such as optimizig etwork performace. I practice, moder DHTs restrict participatio to socalled super-odes, excludig peers with limited coectivity from direct participatio. The primary reaso for this is that virtually all previous DHT routig algorithms (with the otable exceptio of Freeet [15]) are based o the fudametal assumptio of uiversal coectivity betwee all participatig odes (or rely o ustable NAT traversal). This assumptio meas that moder DHTs caot fuctio properly i etworks with limited coectivity (mobile, adhoc wireless, sesor, fried-to-fried, etc.). Followig [15], we refer to these etworks where peers are ot free to directly coect to arbitrary other peers (ad therefore route i the DHT) as restricted-route etworks. We eed to distiguish betwee the etwork topology created by a peer-to-peer overlay ad the uderlyig etwork ifrastructure, so we use the term restricted-route uderlay topology to describe the resultat restrictios imposed o the overlay routig algorithm. This paper itroduces a ew radomized DHT routig algorithm, R 5 N, which eables our DHT to operate effectively over restricted-route etworks ad also icreases security ad resiliece to various attacks compared to existig algorithms. R 5 N oly assumes that the topology is coected ad, i particular, does ot require or use a coordiate system for orgaizig peers. A primary goal of R 5 N is providig a ope etwork where users ca joi or leave at ay time without approval by a certificate authority or other trusted etity. The R 5 N desig itself is relatively simple, essetially combiig a radom walk with recursive Kademlia-style [11] routig. Our desig also icludes topology augmetatio usig a combiatio of distace-vector ad oio-routig, a ovel replicatio strategy ad a API to verify cotet itegrity. Usig distributed emulatio, we demostrate that this ew algorithm has performace comparable to Kademlia if the uderlay is urestricted, ad outperforms Kademlia ad radom walks for various restricted-route topologies. We also show that our algorithm has advatages i terms of availability ad fault-tolerace, especially i the presece of malicious participats. Compared to Kademlia, we geerally see a larger umber of replicas ad higher success rates for data retrieval. Our algorithm has bee implemeted ad released as free software; the release icludes the measuremet tools ad topology geerators used for the experimets preseted i this paper. II. RELATED WORK A DHT imposes structure upo the etwork uderlay by coectig peers to a certai subset of all odes i the etwork. The size ad method of costructio of the routig table is oe of the key desig choices that distiguish DHTs. For example, Kademlia [11] has routig tables of size O(log ) ad ca route requests to the proper destiatio with O(log ) steps. Aother key desig choice for a DHT is the routig or lookup behavior, which is categorized either as iterative or recursive [6]. I iterative routig, the iitiator directly coects to each hop ad retrieves iformatio about the ext hop util the iitiator has a direct coectio to the fial destiatio. As a result, the iitiator of a request has full cotrol over which ode(s) the request is forwarded to at each step ad ca possibly tackle problems (such as ode failures or malicious participats) durig the propagatio (for example, by choosig alterative paths). With recursive routig, the request is forwarded through the etwork from the first hop owards accordig to the routig algorithm ad the iitiator is oly ivolved agai as the fial destiatio of the respose, if there is ay. A key beefit of recursive routig is that the iitiator does ot have to be able

2 to coect to each peer that participates i request routig. However, recursive routig is also less fault-tolerat due to the iitiator s lack of cotrol. A. Kademlia We use a modified versio of Kademlia [11] as the basis of our routig algorithm. The Kademlia algorithm has bee show to work well i etworks with commo rates of chur [12] ad has, i practice, prove capable of hadlig millios of peers [17]. Kademlia uses XOR to determie the distace betwee elemets i the key space. Kademlia s routig table is structured as a array of k- buckets. Kademlia uses as may k-buckets as there are bits i the address space. Each k-bucket ca hold up to k peers. The i-th k-bucket stores up to k peers whose idetifiers are betwee distace 2 i ad 2 i+1 from the local peer. Routig i Kademlia is iterative; at each step the iitiatig ode picks r closest peers for the ext step. Those r peers are queried ad retur a set of peers closer to the key, ad routig cotiues i this fashio util o closer peers are foud. Fially, Kademlia stores data at the r closest peers to the key. Kademlia achieves O(log ) routig performace: i each step the distace to the destiatio is at least halved. Oe failig of Kademlia is that it has bee show vulerable to umerous attacks, such as poisoig [1] ad Sybil [16] attacks. For example, a adversary may wat to dey participats access to a particular key. This ca be achieved by creatig r peers with idetifiers closer tha the closest curret peer to the key; afterwards, all requests will effectively ed at a adversary-cotrolled peer. Access to the data is the uder the cotrol of the adversary. B. Restricted-Route Topologies We use the term restricted-route topology to refer to a coected uderlay topology which prohibits (restricts) direct coectios betwee some of the odes. Commo DHT routig algorithms show dimiished performace or eve arrat failure whe operatig over a restricted-route uderlay. A commo solutio o the Iteret is to restrict participatio i the DHT to peers that are ot ecumbered by NAT or firewalls. However, this solutio limits load-distributio for P2P applicatios o the Iteret ad does ot work at all for physical etworks or fried-to-fried etworks. For these types of etworks, some other method of routig must be employed to cope with restrictios o direct commuicatio. C. Freeet Freeet [15] is the oly efficiet DHT desig we are aware of which works well i restricted-route etworks without coordiates. The mai problem with Freeet s DHT is the iheret vulerability of the critical locatio swappig operatio [5]. This operatio allows a adversary with oly a few peers aywhere i the etwork to cause massive peer idetifier clusterig, leadig to possible data loss ad destroyig the load balacig properties of the DHT. III. DESIGN OF R 5 N The basic idea of R 5 N is to take advatage of the limited coectivity of restricted-route etworks by usig the large umber of peers that are closer to a key tha ay of their eighbors for replicatio. A PUT operatio is used to store data at a radom subset of these peers, ad subsequet GETs the attempt to reach oe of the replicas. PUTs are repeated at a certai frequecy to refresh data. Sice R 5 N performs o-determiistic routig, repeated PUTs are likely to result i data beig stored at differet peers. Furthermore, sice our desig specifies that this refresh period is sigificatly shorter tha the timeout of cotet at the replica odes, this icreases the chace of success for subsequet GET operatios. Naturally, a GET may still fail to fid its target value. I this case, R 5 N expects peers performig GETs to retry a few times. Sice routig of GETs is also o-determiistic, repeatig the GET operatio has a high chace of reachig differet peers ad hece improves the chace of fidig the data. While the GET failure rate is guarateed to declie over time, the specifics deped o a replicatio parameter r, the etwork topology ad the umber ad behavior of adversaries i the etwork. Sice both GET ad PUT operatios take differet paths each time, a adversary has little chace to successfully place his odes i the etwork to block particular key-value pairs. Depedig o how the restricted-route uderlay is costructed, a isolatio attack o odes may still succeed. The remaider of this sectio will detail the various compoets required for the R 5 N routig algorithm. Specifically, we will discuss routig table costructio, request processig, cotet replicatio ad applicatio-level requiremets (specifically cotet validatio). A. The Routig Table Routig tables i R 5 N are costructed ad maitaied i the same maer as i Kademlia (Sectio II-A), with the mai differece beig that R 5 N expects that (especially higher umbered) buckets will be empty eve though peers with appropriate idetifiers exist i the etwork direct coectios were simply ot possible or peers were ot discovered because lookups failed (where they would have succeeded i Kademlia). As i Kademlia [11], this results i O(log ) coectios to eighbors. It should be oted that a small differece i routig table maiteace arises idirectly because FIND PEER messages are routed o-determiistically, i the same maer as GET ad PUT requests. B. Routig Routig i R 5 N is recursive ad is performed i two distict phases. I phase oe, a request for a key is routed for some umber of hops usig radom eighbors from the routig table. I phase two, routig is determiistic usig the peers from the routig table that are closest to the give target. Each request icludes the umber of hops h that the request has traversed so far, ad each peer is supposed to icremet the couter by oe at each hop. Oce the hop couter exceeds a

3 threshold of T log where is the size of the etwork, the request eters the secod phase. The ituitio behid this is that we first make the startig poit i the etwork idepedet from the locatio of the iitiator ad the efficietly fid a earest peer. Assumig the uderlay topology is a restrictedroute topology, there are may peers that are earest to the key as far as their immediate eighborhood is cocered: Lemma 1 (Number of Nearest Peers i a Radom Graph). For a radom etwork with peers ad c radom coectios per peer, the expected umber of earest peers i the etwork to ay radom key is c+1. The optimal umber of radom hops take is equal to the mixig time of the graph [8]. The Markov mixig time for various graphs is well kow. I a full clique, the optimal umber of radom steps to take is 1; i a completely radom graph, it is O(log 2 ) steps [1]. For small-world ad social etworks, the mixig time has bee show empirically to be O(log ) [2]. Sice we expect R 5 N to be used primarily i etwork topologies that more or less coform to small-world topologies, T log radom hops should be eough to arrive at a sufficietly radom poit i the graph. Each request also cotais a uique idetifier ad a 128- bit Bloom filter (each route message is approximately 1k bits i our implemetatio) which are used to improve efficiecy by prevetig loopig ad limitig repeated forwardig of the same request to the same peer. The Bloom filter is updated with the list of peers selected for forwardig the request to at each hop those peers that match the Bloom filter are excluded from the selectio process. C. Processig Requests ad Replies Each peer that receives a routig request performs the same basic sequece of operatios. First, the peer determies whether it is closer to the key of the request tha ay of the peers i its routig table. If the curret peer is a earest peer, PUT requests are ot forwarded; istead the data is stored locally. GET requests where the oly possible result is foud locally are also ot forwarded. Otherwise, the request is forwarded to eighborig peers; these are selected from the routig table usig radom peer selectio or the XOR distace metric depedig o the curret hop couter. The umber of forward replicas is calculated accordig to the replicatio level, etwork size estimate ad umber of hops traversed so far as described i Sectio III-D. For hadlig replies, each peer tracks a bouded umber of active requests, icludig the respective idetity of the precedig peer. Resposes are forwarded alog the request paths util they reach the origial peer or are discarded by a peer that lacks path iformatio (due to memory limitatios, for example). It should be oted that most other DHTs do ot require this additioal state sice, i traditioal DHTs, the ormal routig mechaism ca also be used to route replies. For R 5 N, this is ot feasible due to path radomizatio. Were R 5 N to use radomizatio for replies, the success rate for replies to reach the iteded iitiator would be rather low. I cotrast, radomizatio for the lookup is acceptable sice may peers are expected to store the data due to replicatio. D. Replicatio I R 5 N, replicatio is used ot oly to protect agaist ode failure, but also to improve the chaces of a lookup operatio fidig the desired datum i the absece of failures. For R 5 N, the highest GET success rate would be achieved if there are c+1 replicas i the etwork (Lemma 1). We use r to describe the desired replicatio level ad for R 5 N the target value is r c+1 ; this choice represets a trade-off betwee the cost for PUTs ad the performace for GETs. If the iitiator were to trasmit r PUT requests to obtai r replicas, there would be a good chace of collisio i the resultig paths ad this might be a strog burde o the direct eighbors of the iitiator, especially sice i the uderlay the iitiator may ot eve have r eighbors. Istead, R 5 N attempts to have (o average) 1 + (r 1)h T PUT requests active i the etwork at hop h. Lemma 2. Let h be the umber of hops i the etwork that the query has already traversed. If the etwork is large eough that r radom paths of legth T are ulikely to merge ad if h < T, the the average umber of peers to which a peer forwards a request to should be Υ r,h := 1 + (r 1) T + (r 1)h i order to achieve the desired replicatio level r at T hops. A full discussio ad proof of this Lemma ca be foud i [4]. R 5 N uses a biased radom selectio, forwardig to either Υ r,h or Υ r,h peers to reach o average Υ r,h peers for the ext hop. We cotiue to forward to Υ r,h peers for h 2 T (istead of just util h < T ) to compesate for path collisios, iaccuracies i the etwork size predictio ad ot forwardig PUT requests from earest eighbors. E. Cotet Validatio A key cocer for ay DHT is the itegrity of the cotet stored i the system. R 5 N provides a applicatio with hooks for itegrity checks to detect malformed key-value pairs. The applicatio desiger writes appropriate cotet validatio fuctios for discoverig malformed key-value pairs. Such pairs are the ot forwarded or stored by well-behaved peers, reducig storage ad badwidth requiremets i the presece of faulty or malicious participats ad makig DHT pollutio more difficult. Aother possible issue is allowig multiple values to be stored uder the same key. Requests i R 5 N iclude a Bloom filter which matches replies already kow to the requester. While Bloom filters offer a compact way to filter replies, they ca also produce false-positives. R 5 N mitigates this problem by havig the requester provide a additioal 32-bit mutatio value which modifies the hash fuctio used for testig the Bloom filter. This alters the bit positios which are set i the (1)

4 Bloom filter, makig it less likely the same previous false positive will match. By re-issuig the request with a differet mutatio value, these false-positives ca be elimiated. IV. EXPERIMENTAL RESULTS We ve evaluated R 5 N for various uderlay topologys. I this paper, we will focus o small-world topologies created by extedig a 2D-torus by addig or rewirig radom liks [7]. More extesive experimetal results ca be foud i [4]. Uless stated explicitly otherwise, the preseted experimets were doe usig a fixed replicatio level of r = 1 ad a fixed etwork size estimate parameter T = 4, esurig that oly the shape of the topology ad the ode degree are parameters for the evaluatio. We chose these values for r ad T to eable a fair compariso betwee R-Kademlia ad R 5 N. Specifically, we chose a value of r that is attaiable by R-Kademlia for the various topologies (see Figure 1 for details). Furthermore, as explaied i Sectio III, T is set to correspod roughly to the umber of hops required by R-Kademlia. A. Experimetal Setup We have aalyzed the expected performace of R 5 N usig mathematical aalysis, simulatio ad emulatio [3]. Due to space costraits, the experimetal results preseted i this sectio are oly based o our experimets usig emulatio. For these experimets, we implemeted R 5 N atop a existig P2P framework. The results preseted i this paper were obtaied by emulatig 25 peers o a sigle desktop, which is close to the limits of our hardware ad sice 45 2 = 25 this umber allows for the costructio of a clea 2D-torus topology as a startig poit for our small-world topology costructio. Our emulatio does ot model etwork latecies; however, this is ot a sigificat problem sice R 5 N curretly igores liklatecies i its peer selectio strategy. B. Adversary Model We cosider a umber of types of malicious adversaries with diverse goals i our desig. We assume that each participatig malicious ode has similar resources to that of a ormal participat i the etwork, ad may eavesdrop, alter, sed ad receive messages. This eables floodig ad poisoig attacks (where specific keys are iserted for purposes of deial-ofservice or blockig access). A adversary is assumed to be able to create or impersoate multiple peers ruig simultaeously with free choice of peer idetity for idetificatio ad lookup i the DHT. We also assume that adversaries may collude i order to achieve a specific goal, for istace to perform a sybil or eclipse attack. Fially, we assume that ecrypted messages itercepted at the etwork level are uable to be decrypted by peers other tha the iteded recipiet. While floodig ad poisoig attacks ca be detrimetal to DHTs i geeral, we do ot focus o attacks o data it etirely depeds o the specific applicatio usig R 5 N ad its implemetatio of the validatio hooks (see Sectio III-E) to address this issue. For this paper, we are primarily cocered with routig-level security. C. R-Kademlia We use a variat of Kademlia, which we call R-Kademlia, as a poit of compariso with our ow algorithm. The iterative routig i the origial Kademlia desig performs so badly i a restricted-route topology that it is ot useful for compariso. R-Kademlia is a recursive implemetatio of Kademlia that is otherwise as faithful to the origial desig as possible. The first ad biggest problem with a recursive implemetatio of Kademlia is that r cocurret requests are meat to be kept i flight util o closer peers are foud. R-Kademlia iitiates r requests at the first peer. These requests termiate oce a earest peer is reached; however, the iitiator has o way to guaratee this. Peers i R-Kademlia are resposible for attemptig to forward requests oly to peers that have ot ecoutered the request already usig a Bloom filter (as explaied i III-B). Peers also maitai a limited store of recet requests; thus, if the same request reaches a peer twice, the Bloom filters are merged. Usig these techiques, we mimic the iterative routig of Kademlia, with the exceptio that the iitiator caot cotrol the ext-hop decisios. D. Worst-Case Network Performace For etworks with few coectios, the success rate of R 5 N is sigificatly higher tha it is for R-Kademlia. The worst case for R 5 N whe compared to R-Kademlia is hece a urestricted uderlay topology (clique). I this case, both R- Kademlia ad R 5 N will always fid the data at the earest peer o the first attempt, but R 5 N is expected to take loger. Table I shows the average umber of hops take for the two desigs i this worst-case sceario for R 5 N. TABLE I: Average hops for R-Kademlia ad R 5 N i clique uderlay topologies of differet sizes. As expected, R 5 N takes about twice as may hops as R-Kademlia. Size of Average hops per PUT Average hops per GET etwork R-Kademlia R 5 N R-Kademlia R 5 N ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±.95 E. Replicatio Performace As described i Sectio IV-C, R-Kademlia attempts to achieve a certai replicatio level r by startig r requests i parallel from the iitiatig peer. I cotrast, R 5 N probabilistically chooses multiple peers to forward the request to at each hop. Neither approach is able to precisely hit the specified replicatio target; however, R 5 N produces the same umber of replicas with sigificatly fewer messages whe compared to R-Kademlia (Figure 1) for the small-world topology. This is because sedig out may parallel requests from the same iitial peer icreases the chace that paths will at times coverge, while requests that brach at later hops are likely to be further apart i the etwork ad carry more

5 6 5 Hops Per Replica R-Kademlia Kademlia LLS Regressio Hops Per Replica R5N R5N LLS Regressio 1 8 R5N Roud 1 R5N Roud 5 R5N Roud 1 Kademlia all rouds Total Hops 3 Number of Successful GET Requests Number of Replicas Percetage Malicious Peers i Network Fig. 1: Average hops required per replica; varyig replicatio level r. iformatio about which peers have already bee routed to ad therefore overlap with lower probability. We limit results to 3 total replicas or less; at higher replicatio levels R 5 N outperforms R-Kademlia. 1 Figure 2 compares the total umber of replicas after several rouds of PUT operatios for the same key-value pair (without chur or replica expiratio). The figure shows the umber of replicas that is achieved by either R-Kademlia ad R 5 N for the case where either the same peer performs the PUT operatio or where the source of the put operatio is chose at radom i each roud. If the same peer performs the PUT operatio usig R-Kademlia, the PUT paths always coverge at the same earest peers ad, hece, the umber of replicas remais costat. I cotrast, with R 5 N, radom peer selectio achieves sigificatly higher levels of replicatio over time. If PUTs i R-Kademlia are started at a radom peer, the resultig replicatio levels are oly slightly higher, suggestig that the radom phase achieves its mixig goal. Replicas Preset i Network 1 Due to R-Kademlia s iability to create more replicas tha coectios R5N Radom R-Kademlia Radom R5N Same R-Kademlia Same Number of Rouds Fig. 2: Replicatio over time; same startig peer vs. radomized startig peers. Note that stadard deviatios are quite small due to usage of the same topology i each trial. Fig. 3: Number of malicious peers at radom locatios i a etwork with 25 peers vs. percetage of successful GET requests. Each of the µ malicious peers drop all requests they receive, represetig the simplest type of malicious participat. F. Robustess Agaist Active Adversaries A additioal goal for our routig algorithm is to perform well i the presece of malicious participats. Hadlig malicious participats well subsumes hadlig peer failure due to bugs, chur or miscofiguratio. The desig of R 5 N already explicitly addresses malicious peers that attempt to perform deial of service (DoS) attacks o the etwork by boudig the resource multiplier effect of all operatios. Peers caot sed requests that cosume sigificatly more resources tha ormal requests, so a adversary ca oly multiply its ow badwidth by less tha the average umber of hops for requests multiplied by the replicatio level r. Similarly, poisoig attacks may be mitigated usig cotet validatio hooks (Sectio III-E). A active adversary could also joi the etwork with peers that simply passively drop all requests that are received. For these experimets, we vary µ, the umber of malicious adversaries which drop requests. This kid of attack is already quite detrimetal to overall operatio for determiistic algorithms: ay request that traverses ay of the malicious peers fails. R- Kademlia s redudacy (r-replicatio) is a typical mitigatio strategy. Figure 3 shows the impact of a droppig adversary o the performace of R 5 N ad R-Kademlia i terms of success rates for GET operatios (iitiated at peers selected uiformly at radom i each roud) for a small-world topology geerated to have 25 odes ad 3k edges. The GET operatios were performed after a umber of rouds of PUT operatios which are iitiated at the same peer i each roud. Later GET rouds i R 5 N have higher success rates because additioal PUT rouds icrease availability for R 5 N as more replicas are created. The beefit of R 5 N over R- Kademlia is clearly see i the small-world topology where R 5 N achieves sigificatly better performace uder this type of attacker. Results for other topologies ca be foud i [4]. We ow cosider a attacker tryig to prevet access to a particular key usig a Eclipse attack. The attacker agai

6 Number of Successful GET Requests R5N Roud 1 R5N Roud 5 R5N Roud 1 Kademlia all rouds Percetage Malicious Peers i Network Fig. 4: Number of malicious peers preset at sybil locatios i a etwork with 25 peers vs. percetage of successful GET requests. simply drops all GET ad PUT requests; however, this time the µ malicious odes are ot placed ito the etwork at radom but at the µ peers that are closest to the key. This represets a attacker performig a Sybil attack with free choice of idetifier ad ode placemet i a restricted-route topology the strogest type of Sybil attacker we ca imagie. This attack has a serious impact o Kademlia-based DHTs [16]. While additioal traditioal protectios agaist Sybil attacks could be deployed to further mitigate this attack [4], Figure 4 shows that such measures may be uecessary for R 5 N. Agai, as rouds of PUT requests icrease the umber of replicas i the etwork, R 5 N s success rate icreases. Agai, R 5 N outperforms R-Kademlia ad is especially strog i the case of a Sybil attack o the small-world uderlay topology, where eve the first roud of GET requests succeeds with a much higher rate tha R-Kademlia. V. PERFORMANCE ANALYSIS To achieve high success rates, R 5 N eeds to create a sufficiet umber of replicas. A etwork with odes of degree c is expected to have c 1 earest peers. Assumig that T is chose large eough to achieve perfect mixig, c 1 replicas would eed to be created i order for a GET request to succeed with about 5% probability accordig to the birthday paradox. Oce idividual requests succeed with this probability, a small costat umber of repetitios ca be used to get high overall success rates. As we have show experimetally, the relatioship betwee the umber of replicas i the etwork ad the umber of hops required for the respective PUT operatios is almost liear (Figure 2). Sice idividual PUT ad GET requests have complexity O(log ), routig i small-world etworks usig R 5 N scales with O( log ). Note that this does ot hold i sparse graphs with large diameter or graphs that are ot expader graphs (such as a circle) because the routig tables could ot be sufficietly populated. VI. CONCLUSION We have preseted a robust routig algorithm for restrictedroute etworks. Our R 5 N algorithm combies a radom walk with a recursive variatio of Kademlia ad uses forwardig to multiple targets alog the path for replicatio ad redudacy. R 5 N has good performace ad is robust agaist a rage of some well-kow attacks o DHTs, icludig poisoig attacks, Sybil attacks ad Eclipse attacks. Ackowledgmets This work was fuded by let ad the Deutsche Forschugsgemeischaft (DFG) uder ENP GR 3688/1-1. REFERENCES [1] C. Avi ad G. Ercal, O the cover time ad mixig time of radom geometric graphs, Theor. Comput. Sci, 7. [2] M. Dell amico ad Y. Roudier, A measuremet of mixig time i social etworks, i 5th Iteratioal Workshop o Security ad Trust Maagemet, Sait Malo, Frace, September 9. [3] N. Evas ad C. Grothoff, Beyod simulatio: Large-scale distributed emulatio of p2p protocols, i 4th Workshop o Cyber Security Experimetatio ad Test (CSET 11). USENIX Associatio, 11. [4] N. S. Evas, Methods for secure decetralized routig i ope etworks, Ph.D. dissertatio, Techische Uiversität Müche, 11. [5] N. S. Evas, C. GauthierDickey, ad C. Grothoff, Routig i the dark: Pitch black, i 23rd Aual Computer Security Applicatios Coferece. IEEE Computer Society, 7, pp [6] J. Hautakorpi ad G. Camarillo, Evaluatio of dhts from the viewpoit of iterpersoal commuicatios, i Proceedigs of the 6th iteratioal coferece o Mobile ad ubiquitous multimedia. ACM, 7, pp [7] J. M. Kleiberg, Navigatio i a small world, Nature, vol. 6, o. 6798, pp ,. [8] D. A. Levi, Y. Peres, ad E. L. Wilmer, Markov chais ad mixig times. America Mathematical Society, 6. [9] J. Li, J. Striblig, T. M. Gil, R. Morris, ad M. F. Kaashoek, Comparig the performace of distributed hash tables uder chur, i Proc. of the 3rd IPTPS, 4. [1] T. Locher, D. Mysicka, S. Schmid, ad R. Wattehofer, Poisoig the Kad Network, i 11th Iteratioal Coferece o Distributed Computig ad Networkig (ICDCN), Kolkata, Idia. Spriger, Jauary 1, pp [11] P. Maymoukov ad D. Mazières, Kademlia: A Peer-to-Peer Iformatio System Based o the XOR Metric, i 1st Iteratioal Workshop o Peer-to Peer Systems, Cambridge, March 2, pp [12] Z. Ou, E. Harjula, O. Kassie, ad M. Yliattila, Performace evaluatio of a kademlia-based commuicatio-orieted p2p system uder chur, Comput. Netw., vol. 54, pp , April 1. [13] S. Ratasamy, P. Fracis, M. Hadley, R. Karp, ad S. Sheker, A scalable cotet-addressable etwork, SIGCOMM Comput. Commu. Rev., vol. 31, o. 4, pp , August 1. [14] S. Rhea, D. Geels, T. Roscoe, ad J. Kubiatowicz, Hadlig chur i a dht, i Proceedigs of the aual coferece o USENIX Aual Techical Coferece, ser. ATEC 4. Berkeley, CA, USA: USENIX Associatio, 4, pp [15] O. Sadberg, Distributed routig i small-world etworks, i Proceedigs of the Eighth Workshop o Algorithm Egieerig ad Experimets, 6, pp [16] M. Steier, T. E-ajjary, ad E. W. Biersack, Exploitig kad: possible uses ad misuses. Computer Commuicatio Review, vol. 37, o. 5, pp. 65 7, October 7. [17] M. Steier, T. E-Najjary, ad E. W. Biersack, A global view of kad, i Proceedigs of the 7th ACM SIGCOMM coferece o Iteret measuremet. New York, NY, USA: ACM, 7, pp [18] D. Stutzbach ad R. Rejaie, Uderstadig chur i peer-to-peer etworks, i Proceedigs of the 6th ACM SIGCOMM o Iteret measuremet. New York, NY, USA: ACM Press, 6, pp [19] H. Yu, M. Kamisky, P. B. Gibbos, ad A. Flaxma, Sybilguard: Defedig agaist sybil attacks via social etworks, i The ACM SIGCOMM 6 Coferece. ACM Press, 6, pp

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