A Method for Privacy Preserving Mining of Association Rules based. on Web Usage Mining

Transcription

1 200 Intenatonal onfeence on Web Infomaton Systems and Mnng A Method fo Pvacy Pesevng Mnng of Assocaton Rules based on Web Usage Mnng Wang Yan Le Jajn Huang Dongme Gloous Sun School of Busness and Management DonghuaUnvesty Shangha,hna yanwang@mal.dhu.edu.cn School of ompute Scence and Technology DonghuaUnvesty Shangha,hna lejajn@dhu.edu.cn ollege of Infomaton Technology Shangha Ocean Unvesty Shangha,hna dmhuang@shou.edu.cn Abstact:Data mnng basng on Pvacy pesevaton has become a eseach hot pont now. Web usage mnng s one nd of data mnng applcatons, and how to pevent data leaness n web usage mnng s also an mpotant ssue. In ths pape, we pesent an effectve method fo pvacy pesevng assocaton ule mnng n the web usage mnng, Seconday Random Response olumn Replacement (SRRR) to mpove the pvacy pesevaton and mnng accuacy. Then, a pvacy pesevng assocaton ule mnng algothm based on SRRR s pesented, whch can acheve sgnfcant mpovements n tems of pvacy and effcency. Fnally, we pesent expemental esults that valdate the algothms by applyng t on eal datasets. Keywods: web usage mnng; pvacy pesevaton; andomzed esponse; assocaton ules I. INTRODUTION Wth the contnuous development of data mnng technology, ts applance s moe and moe wdely. Web data mnng s an applcaton hot pont of data mnng n ecent yeas. The fast development of web maes tself to be the wold's lagest publc data souce, Web data mnng s based on publc data souces, but thee ae also pvacy leaness poblem. Fo example, on e-commece ste, all ecods of customes vsted the webste ae stoed n Web seve log wthout esevaton. These ecods eflect the consumes buyng habts and puchasng powes,but the customes do not want these data to be dsclosed, so pvacy potecton should also be consdeed n the web data mnng. Pvacy pesevng assocaton ule mnng s to dscove the fequent temsets as accuately as possble and geneate the mnmum suppot and confdence by accessng the ognal tansacton sets of condtons. Thee s a contadcton between data pvacy and data accuacy. At pesent, the pvacy potecton of data mnng methods can be dvded nto data petubaton [-] and quey estcton [-5]. In 2002, S. J. Rzv and J. R. Hatsa pesented a epesentatve assocaton ules mnng method, MASK (Mnng Assocatons wth Sececy Konstants) [] that s a data petubaton stateges. Because Wane model n statstcs s used n MASK, all the data tansfomed have the dectly elatons wth the eal aw data, whch maes the pesevng less than satsfactoy, and the selecton of andomzed paametes should devate fom 0.5 [6] s anothe lmtaton. In ths pape, we gve the geneal famewo fo mnng assocaton ules n the web usage mnng and use a use-oented and tme-oented sesson exploe method to geneate sequence sets fstly. Then we popose a method to tansfom use sesson nfomaton nto a elatonal data table, and petub data by usng Seconday Random Response olumn Replacement (SRRR) algothm. Based on the pseudoandom data set, fequent temsets and stong assocaton ules dscovey algothm ae pesented. II. THE GENERAL FRAMEWORK As e-commece, Web sevces and Web-based nfomaton system s sustanable development and gowth, thee ae a lage numbe of use data collected n web oganzatons. The pncpal data souces n web usage mnng ae web seve log fles, and othe Data souces ncludng the web ste fles and metadata, opeatonal databases, applcaton templates and doman nowledge. In ode to solve pvacy pesevaton ssues of web usage mnng, we poposed Seconday Random Response olumn Replacement (SRRR) algothm, and a pvacy potecton assocaton ule mnng methods based on t. The oveall stuctue and famewo shown as follow Fg..The famewos wee dvded nto 2 stages: data pepocessng stage and pvacy pesevng &mnng stage. The data pepocessng stage pepocessng the data by fve steps and geneate the sesson tanslate table. The pvacy pesevng stage s the ey stage ncludng the pvacy pesevaton methods and assocaton ule applcaton /0 $ IEEE DOI 0.09/WISM

2 Fg. Web usage mnng famewo III. PRIVAY PRESERVATION IN WEB APPLIATIONS MINING A. Data Pepaaton Each clc acton of web use n e-commece stes s ecoded n the web log, so the ognal web log ecods contan a lot of bowsng nfomaton. Befoe web data mnng, data should be pepocessed [8], whee the pocess ncludes flteng, despdeng, use dentfcaton, sessonzaton and path completon. B. Tme-oented use sesson explong method Afte data pepocessng, we can mne and extact elevant data n web seve logs. Fo the puposes of analyss, these data need to be conveted and aggegated n the dffeent levels. In web usage mnng, the page s the basc level of data extacton and the sesson s the basc acton n the data extacton fo uses. TABLE ORIGINAL SESSION SAMPLE ID IP URL Tme Page 0: Page2 0: Page 0: Page 0: Page 0: Page 0: Page5 0: Page2 0: Page5 0: Page2 0: Page 0: Page 0: Page 0: Page5 0:25 Table shows a sample of use s ognal sesson. In the table each ow epesents a use sesson dentfed by usng unquely ID, and IP epesents the use's IP addess, whee dffeent addesses fo dffeent uses. URL means the page accessed by uses n the sesson. The deal method of use path estong can econstuct the eal pocess of use sesson. Hee we use tme-oented use sessons exploe method. Let the vecto M{M,M 2,,M,, M N }epesent the log data set, whee M {IP, URL, Date, Tme, Method, ode, Bytes }, and N s the numbe of log fles. Web logs contan the IP addesses, vsted URLs, date and tme ecods, access methods (GET o POST), access stuctue and the sze of access nfomaton. the vecto S{S,S 2,,S,,S K } epesents the sesson data set, whee s the numbe of sessons, and S {IP,Page,t },ths stuctue contans the IP addess, vsted page and the tmestamp n each sesson. Page {Page,Page 2,,Page,,Page L}, L s the numbe of pages fo the sesson. The algothm s descbed below: Fo each M of M If Method s GET and Ul s WEBPAGE If S S and IP IP Set t 0 // set the fst tme stamp of equest n the sesson If( t t 0 <20mn) // set the lmtaton of access tme s 20 mnutes S (IP,PAGE S U PAGE S,t ) Delete S fom S Add S nto S Endf Add S nto S Endf Endf Endfo Table2 s the sesson sets geneated by the path explong algothm. TABLE2 SESSION SETS GENERATED BY PATH EXPLORING ALGORITHM SID IP Page Tme Page,page2,page 0: Page, page,page,page5 0: Page2,page5 0: Page2,page 0: Page,page,page5 0:25. Tansfomng use sessons set nto twodmensonal elaton table Fo the use sesson set n Table 2, we can use elatons table S_table to stoe the use sesson path, the table stuctue s (TID, IPID, SESSIONID, P ),

3 whee we stoe the IP addess, sesson ID, and the vsts of each page n a two-dmensonal table. The value of P column means whethe the use vsts the page and buys the poducts o not.n the column means an access to the page and 0 means no access to the page. The algothm 2 s descbed below; t tansfes the use actvty ecods nto the elaton twodmensonal table that can be handled easy. Algothm 2 Input: use sesson set S Output: two-dmensonal use sesson table S_table eate table S_table(TID,IP,SESSIONID,P ) Fo each S of S Inset nto S_table(IPID,SID) values (IP,SID ) If P of S Inset nto S_table(P )Values( ); Inset nto S_table(P )Values( 0 ); Endfo Each sesson wll be seen as a tansacton, the custome's sesson sequence can be conveted to elatonal tables whee the page numbes epesent the coespondng puchase acton. We added a new feld TID n the table, whch s the dentty column and can be assgned values automatcally by the system. Table 2D table savng the use sesson path TID IPID SID P P2 P P P D. Pvacy Potecton Algothm In the two-dmensonal table the columns whch need to pvacy potected ae P, P2 and P, P and P5, those columns ae nown as senstve attbutes n the pvacy potecton technology [7].The attbute values tself ae not to dsclose pvacy, but the attbute goup wll lea customes pvacy nfomaton. Theefoe we potect the senstve attbutes by usng the mpoved andomzed esponse method. In ode to mpove the effcency of data pesevaton, we andomze the value twce at a cetan pobablty, and ths algothm s named the seconday andom esponse column eplacement algothm (SRRR). Specfc methods ae as follows: Set andom paamete I, 0 p, p2, p, p,and p + p2 + p + p, Set andom paamete II, eate andomzaton functon R (x), the ognal column (, j) s tansfomed to hdng column F (, j), whee F(,j) {0,},(,j) {0,}. Fo x F(,j) {0,}, andom functon conventon s as follows: TABLE ONVENTION OF RANDOM FUNTION p p 2 p P x x 0 y 2 y x 0 null Let s an tem, whee π s the suppot of n, π null s the suppot of null n, λ s the suppot of n F. The tansacton T of s conveted by SRRR pocess to the tansacton T of F, and then thee s: λ π p * () null π λ ( p + ) + + (2) * p * p p * By the () and (2): π - p - π null p + π null π - p - κ φ p + γ φ () Hee κ s the top coeffcent, γ s the bottom coeffcent,and φ π null s the suppot of null value.we can see that the beneft of the algothm s the ntoducton of the adjustable factos to calculate of the suppot. In t the actual petubaton pocess, we can use a null value to futhe enhance pvacy pesevaton. TABLE 5 PROBABILITIES OF DATA MAPPING BY SRRR METHOD Assumng that suppot theshold s 0. n the M, and choosng p 0.2,p 2 0.2,p 0.,p 0.5, whle select 0., 2 0.2, 0.2, 0., accodng to SRRR algothm custome sesson set s conveted as table6:,,,,and + + +,

4 TABLE6 FAKE OLUMN DATA SET GENERATION AND FINAL FAKE OLUMN DATA SET Afte conveson algothm SRRR, fae sets nclude not only the value of 0 and, also null value. The meanng of Null can be defned by the use. Mnng assocaton ules can tae nto account the column of null value and the suppot of temset n fae set afte conveted fom SRRR algothm can matches the suppot of j n the ognal tansacton set. IV. PRIVAY PRESERVING ASSOIATION RULE MINING ALGORITHM Apo algothm s an nfluental algothm fo mnng fequent temsets and Boolean assocaton ules. Based on the Apo Algothm [9], we pesented an mpoved algothm to acheve assocaton ule mnng fo fae tansacton sets conveted by SRRR. Gven the mnsup and data set, man mplementaton actons of the algothm ae descbed as follows: Algothm. Pogessve scan assocaton ule mnng algothm.. Settng fequent temsets ϕ, and ϕ s collecton of temset M n whose tem numbe s n,whee n,and s the maxmum numbe tansactons. 2. Seachng the lagest temset pogessvely. Algothms scan the data ow by ow, and matchng each lne wth all tems set, f the ow matches wth some temsets, then plus fo each locaton of sequenceϕ.. Usng Apo punng pncple n the pocess of match the temset n the ow data. Algothm fst match -temset, then 2- temset, -temset.to educes the compute load.. culaton n tun,when the ato of the numbe n - temset of the seached ows to the numbes of all ows has eached the mnmum suppot of - temset, stop ths -temset match pocess.if the ato o f the numbe n n - temset of the seached ows to the numbes of all ows m has not eached the mnmum suppot of - temset, only eached the numbe of x, when n/m> mnsup, algothm calculate ((n-m)+x)/m, f > mnsup, contnues cculatng, stop ths temset seach. 5. Gettng all the fequent temsets whch satsfy the mnsup. Pocessng of Algothm s descbed below : Input: temset D afte conveson algothm SRRR, mnmum suppot theshold s. Output: fequent temset F of eal dataset D. Setϕ, ϕ {M,M 2,,M n } {(L ),(L 2 ),..(L ), (L,L 2 ),.. (L...L )} s the full temset fo (n; >0;--),and n set A ndex() // set the seachng matx matchngϕ () fo each tansacton t D // scan each ow R *A f R 0, then // ext matched ow f n //countng evey tem calculate template (c.count c.count 0 +, c.count c.count +. c.count n c.count n +) select_add( c.count) // functon select_add endf endf endfo F { c c. count / n mnsup} endfo etun F U F Functon select_add s to count each temset afte punng (), that s c.count c.count 0 +, c.count c.count +. c.count n c.count n +. In the countng pocess, algothm pune the fequent temsets geneated (Punng methods s n Apo pncple, no explanaton). Afte obtaned fequent temsets, based on the suppot and confdence fomula below, data povdes can fnd the assocaton ules of web log tem. Suppot (A B) Suppot_count (AUB) / all_count V. PERFORMANE ANALYSIS A. Algothm omplexty Analyss Theoetcally, Apo algothm s an exponental algothm. let N be the sze of tansacton, each tansacton ncludes n tems aveagely, and then the whole temset space wll each ο ( 2 n ).The mnng algothm we have mpoved, when <-2 * (-mnsup), the temset coesponds wth the poston of the value n sequence ϕ does not match the mnmum suppot, the last calculatons fo ths temset wll be gven up. If we do not consde ths nd of ow optmzaton, tme complexty of the algothm s 6

5 n/2 ο (N* n )ο (N*2n/2). If the pobablty pe-judgment mentoned above s taen nto account dung the ow judge, the complexty wll decease contnually. B. Expemental esults We obtaned the web seve log fles n some 2 webste n 0 days and conveted tansacton sets T by the algothm and 2. The tansact numbe s 000, the tem numbe s, and the ATL (the aveage tansacton length) s 2.8. We analyzed the pvacy potecton wth mnmum suppot of %, and the followng table shows the pvacy potecton ntensty of the mnmum suppot of % wth dffeent values of andomzed algothm paametes fo dffeent. TABLE 7 INTENSITY OF PRIVAY PROTETION The Fg.2 shows the aveage temset eo of SRRR method and MASK method, and states the elatonshps between the andom paamete, the data pvacy ntensty and the accuacy of mnng esults. Fg. 2 Aveage temsets eo of SRRR method and MASK method As can be seen, MASK method has wde eo by compason. When the P s close to 0 o, the mnng esults s moe accuate, but the ntensty of pvacy potecton s poo. When the value of P gadually appoaches 0.5, pvacy potecton ntensty has been mpoved, but the accuacy of mnng esults wll be sgnfcantly deceased. Eo changes of the method SRRR poposed n ths pape s elatvely stable, accodng to the P value, that s, when the popoton of eal data vay fom 0 to, the pvacy damage coeffcent wll nceased fom 0 to smultaneously, so t lead to the decease of the ntensty of pvacy potecton, and contnuously mpove the accuacy of mnng esults. VI. ONLUSIONS Pvacy pesevng n web data mnng has asen woldwde concens wth the pomoton of netwo technology and the demand of applcaton. But thee ae many dawbacs and open questons. In ths pape, we have pesented a method fo pvacy pesevng mnng of assocaton ules based on web usage mnng. Fst, we gave the geneal famewo fo mnng assocaton ules n the web usage mnng, geneated sesson sets by explong use sessons and tansfe sesson sets to elaton two-dmenson table. Second, we poposed seconday andom esponse column eplacement (SRRR), a smple and effectve pvacy pesevng algothm, and acheve pvacy potecton assocaton ule mnng based on SRRR. Fnally, we pesented expemental esults that valdated the algothm (SRRR) n pactce by smulaton. In the futue, we wll enhance the effcency of mnng algothm futhe by paallelzaton and othe methods, and combne SRRR wth othe pvacy pesevng ways to acheve moe sgnfcant mpovements n tems of pvacy, accuacy, effcency, and applcablty. AKNOWLEDGMENT The study s funded by ey poject of Shangha Scentfc ommttee, subject numbe: 08dz REFERENES [] Rzv SJ, Hatsa JR. Mantanng data pvacy n assocaton ule mnng. In: Bensten PA, Ioannds YE, Ramashnan R,Papadas D, eds. Poc. of the 28th Int l onf. on Vey Lage Data Bases. Hong Kong: Mogan Kaufmann Publshes, 2002, pp [2] Agawal S, Kshnan V, Hatsa JR. On addessng effcency concens n pvacy-pesevng mnng. In: Lee YJ, L JZ, Whang KY,Lee D, eds. Poc. of the 9th Int l onf. on Database Systems fo Advanced Applcatons. LNS 297, Jeju Island: Spnge-Velag,200, pp.- 2. [] Evfmevs A. Randomzaton n pvacy pesevng data mnng. SIGKDD Exploatons, 2002,(2), pp. -8. [] Saygn Y, Veyos VS, lfton. Usng unnowns to pevent dscovey of assocaton ules. AM SIGMOD Recod, 200,0(),pp [5] Olvea SRM, Zaane OR. Pvacy pesevng fequent temset mnng. In: lfton, Estvllasto V, eds. Poc. of the IEEE Int lonf. on Data Mnng Woshop on Pvacy, Secuty and Data Mnng. Maebash: IEEE ompute Socety, 2002, pp. -5. [6] Zhao JK. Theoy and Methods of Samplng Desgn n Statstcal Suvey. Bejng: hna Statstcs Pess, 2002 (n hnese). [7] Sweeney L Achevng -anonymty pvacy potecton usng genealzaton and suppesson[j]. Intenatonal Jounal off Uncetanty.Fuzzness and Knowldege-Based Systems (5), pp. 57l-588 [8] Godon S. Lnoff Mchael J.A.Bey Mnng the Web: Tansfomng ustome Data nto ustome Value[J]. Publshng House of Electoncs Industy. 200, pp. 2-7 [9] R.Agawal and R.Sant. Fast Algothms fo Mnng Assocaton Rules. In Poc.of the 20th Intl.onf.on Vey Lage Data Bases(VLDB 9),99, pp