A DISTRIBUTED REPUTATION BROKER FRAMEWORK FOR WEB SERVICE APPLICATIONS

Transcription

1 L et al.: A Dstrbuted Reputato Broker Framework for Web Servce Applcatos A DISTRIBUTED REPUTATION BROKER FRAMEWORK FOR WEB SERVICE APPLICATIONS Kwe-Jay L Departmet of Electrcal Egeerg ad Computer Scece Uversty of Calfora, Irve, Calfora, USA kl@uc.edu Jae Y.J. Hsu Departmet of Computer Scece ad Iformato Egeerg Natoal Tawa Uversty, Tape, Tawa yjhsu@cse.tu.edu.tw Yue Zhag Departmet of Electrcal Egeerg ad Computer Scece Uversty of Calfora, Irve, Calfora, USA yuez@uc.edu Tao Yu Departmet of Electrcal Egeerg ad Computer Scece Uversty of Calfora, Irve, Calfora, USA tyu@uc.edu ABSTRACT Ths paper presets a dstrbuted reputato ad trust maagemet framework that addresses the challeges of elctg, evaluatg ad propagatg reputato for web applcatos. We propose a broker framework where every user s assocated wth a reputato broker who collects for ts users the reputato ratgs about ay web servce. I retur, a user provdes ts broker the servce ratg after each trasacto wth ay servce order to buld up the reputato for the servce. I our mechasm desg, brokers form a trust etwork where they exchage ad collect reputato data about all servces. By delegatg trust maagemet to brokers, dvdual users oly eed to check wth ther brokers about the reputato of a servce before ay trasacto. We preset the dstrbuted reputato ad trust maagemet framework ad show the performace of the system by smulatos. Keywords: Trust, Reputato, Web Servces, Dstrbuted Systems. Itroducto I world, trust s a relatoshp betwee two ettes: t s oe s belef o certa attrbutes about the other. There are several propertes to be cosdered a trust relatoshp:. Idetfcato: whether the subject etty s what t clams to be. 2. Qualfcato: whether the subject etty s capable of performg specfc servces. 3. Cosstecy: whether the subject etty s able to delver a servce wth a acceptable certaty. The frst property s usually esured by requestg certa formato from the subject ad detfyg that the subject s amog a specfc group that ca be trusted to have certa prvleges. The secod property s ofte aswered by askg for a proof about the subject s capablty ad performg some valdato procedure usg predefed polcy. The last property s the most dffcult oe sce t should ot be clamed by the subject tself. The cosstecy of servces or performaces provded must be verfed by others, ether by a formal certfcato process or by feedbacks from peer clets. I ths paper, we call the thrd property the reputato about a peer. For e-servces, the desg of a trust maagemet framework s a dscple that has gaed much atteto recet years [Dellarocas 23] due to the growth of ole trasactos ad e-commerce actvtes [Che 23, Wag 2]. Tradtoal dstrbuted ecrypto ad authetcato mechasms ca oly solve the detfcato ad, to some extet, the qualfcato problems. However, t s ulkely that a etty wll act cosstetly all trasactos. Reputato systems [Yu 2, Jurca 23, Xog 24, Dellarocas 23] are thus far the most preferred mechasm addressg ths problem. Reputatos systems where feedback ratgs are aggregated over a perod of tme to reflect the trustworthess of a servce have bee mplemeted may e-marketplaces. The success of Amazo ad ebay proves that such reputato systems are helpful fosterg trust for vedors. Both Amazo ad ebay are examples of cetralzed reputato systems. Wth a sgle trust authorty cotrols all reputato formato, such systems may be vulerable or flexble. I addto, cetralzed authorty may be subject to the scalablty problem. To solve these problems, dstrbuted trust maagemet systems have bee proposed ad studed [Yu 2, Xog 24]. I a dstrbuted trust system, reputato formato s scattered Page 64

2 Joural of Electroc Commerce Research, VOL 7, NO.3, 26 amog partes the system. The dstrbuted approach brgs ew challeges, cludg how to elct reputato formato, how to evaluate the trustworthess of a party wth formato gathered from potetally utrustworthy partes, ad how to propagate reputato formato throughout the commuty. Ths paper presets the desg of a geeral trust framework ad the mplemetato of trust brokers. Due to the complexty of trust maagemet, we propose the desg of software trust brokers to maage the trust relatoshp for e-servce users. I world commutes, people ofte rely o recommedatos by word-of-mouth from trusted acquataces or trusted experts to evaluate the trustworthess of a servce provder. Dfferet trust levels exst our -lfe actvtes wth close freds, freds of freds, people wth kow qualfcatos, ad total stragers. Our desg s motvated to emulate these -lfe trust buldg processes ad reputato mechasms by usg software brokers as trusted experts. We propose that ole users make use of trust brokers, whch may be mplemeted usg some commo, certfed software package lke Lberty Allace [Lberty Allace 24]. Lke search ege stes ad portals, trust brokers are depedetly mataed ad operated; users may choose amog may brokers ether freely avalable or wth pad servces. A broker collects servce reputato formato for ts users. Each user, after each trasacto wth a voked servce (from some servce provder or vedor), wll produce a reputato ratg o the servce ad sed t to a broker so that the broker may buld up the reputato about that servce. I ths way, the reputato of a servce ca be produced from the report of all ts prevous clets. Aother ssue that we study s how to buld the referral etwork amog trust brokers. Trust brokers teract ad share reputato formato just lke world travel agecy brokers ad stock brokers. Due to the dstrbuted ature of trust brokers, t s feasble to collect all reputato formato from all brokers. We eed effcet mechasms to collect ad maage the dstrbuted reputato formato trust brokers. However, gve possbly cotradctory experece, brokers may ot have the same level of trust o each other. Brokers usually solct servce reputato formato from those brokers they trust more. Our proposed mechasm allows brokers to buld up dfferet trust levels o each aother, ad to select oly those they trust to share reputato formato. The rest of ths paper s orgazed as follows. Secto 2 provdes a overvew of the curret research o trust maagemet. We troduce our trust system framework Secto 3 ad a trust broker desg Secto 4. Some smulato results are preseted Secto 5. Secto 6 presets a exteded model whe a servce s behavor may be affected by the trasacto value. The smulato study for the exteded model s preseted Secto Prevous Work Research actvtes dstrbuted trust maagemet clude broadly the followg areas.. Formalzg trust [Dellarocas 24]. There are may dfferet ways to calculate trust. I practce, Amazo smply takes a average of product ratgs based o customer revews. BzRate comples the average satsfactory dex about the merchat addto to product ratg; whle ebay presets the feedback score ad the percetage of postve feedbacks. Researchers proposed varous mprovemets, e.g. by gvg hgher weghts to feedbacks from users wth better reputato. A successful reputato system should make t hard to buld up good reputato so that a user s less lkely to abuse ts hard eared reputato. 2. Referral etwork systems where agets cooperate to propagate reputato formato the commuty [Yu 2]. Each aget s assumed to have eghbors, whch are the coected to ther ow eghbors. A aget dyamcally restructures ts eghbors based o ther trustworthess. A drect eghbor s ot as trustworthy as some drect eghbors f the drect eghbor s opos are ot cosstet wth the aget s ow experece. 3. Usg cotext factor to model the dyamc behavor of ettes [Xog 24]. It has bee proposed that Trasacto Cotext may be used as a mportat factor whe aggregatg the feedback from each trasacto as trasactos may dffer from oe aother. For example, f a commuty s busess savvy, the sze of a trasacto s a mportat cotext that should be corporated to wegh the feedback for that trasacto. However, ther model mxes reputatos wth dfferet cotext weghts together. Oe major cocer s ths game rule allows the system to use multple small cotext weght postve feedback to hde the effect of oe bg cotext weght egatve feedback, whch may eed further research to prevet t from happeg. 4. Icetve mechasms for elctg hoest feedbacks [Jurca 23]. Studes of ebay s reputato system have show that t s dffcult to elct feedbacks. A mportat reaso for such dffculty s the lack of cetves for the users. I some commutes, users are reluctat to share formato for fear that t wll gve compettve advatage to others. Icetve mechasms address ths ssue by provdg cetves to users that gves hoest feedbacks through some sde paymet mechasm. 5. Mechasms to guard agast coordated attack agast the system [Xog 24]. Based feedbacks ca be fltered out wth a large umber of feedbacks. Eve a smple approach such as to take the average of all Page 65

3 L et al.: A Dstrbuted Reputato Broker Framework for Web Servce Applcatos ratgs s able to flter out subjectve ad based ratgs. I cotrast, coordated attacks o the system are much harder to guard agast. A group of users mght form a colluso gvg oly postve feedbacks to the members the group ad egatve feedbacks to others outsde the group. Our research adopts ad tegrates may of the deas from past research of the frst three topcs. We propose a defto of servce reputato based o past trasacto outcome. We also tegrate reputato data from dstrbuted brokers usg a mechasm smlar to the referral etwork. 3. System Model ad Assumptos Usg a dstrbuted trust system, reputato formato geeral s dstrbuted wth the commuty. The challege for a user s to gather eough formato for makg a formed judgmet o the trustworthess of a servce. More specfcally, the trust buldg process volves two separate problems:. how to gather reputato formato, ad 2. how to utlze the formato gathered Drect Trust Coected Trust Isttuto Trust Trustless Fgure : Trust Herarchy We have modeled the trust relatoshp to three levels as show Fgure. The frst level, the drect trust level, s for users that subscrbe the same trust broker where there s a drect measuremet of trust amog all members. The secod level, the coected trust level, s whe two users utlze two dfferet trust brokers. So users must fd formato about each other through some dstrbuted trust collecto protocols. If there s ot eough trust that ca be gathered at ths level, the thrd level, the sttuto trust level, reles o a cetralzed trust authorty to provde global certfed trust servce about each other for decso makg. If the thrd level stll caot meet the trust polcy, users wll have to use some trustless protocol [Atallah 23] to coduct busess. Our work ths paper s mostly o the coected trust level. Fgure 2 shows our dstrbuted trust ad reputato maagemet system cosstg of three types of compoets: users, brokers, ad reputato authortes. I our model, all users also fucto as servers themselves (just lke agets or P2P systems). A user the role of a clet ca geerate ay request to tate a trasacto wth aother user, assumg the role of a server. I ths archtecture, users rely o ther trust brokers to collect reputato formato. A broker typcally works for multple users who are wllg to share reputato formato amog the group. Each broker matas a reputato database that collects the reputato of all servces that have had trasactos wth ts user clets. Ths project proposes a dstrbuted trust ad reputato maagemet framework that addresses the challeges o maagg trust amog e-servces. Our work s related to the referral etwork approach [Yu 2] by usg a etwork of brokers to propagate reputato formato. However, we collect the reputato o both servces ad brokers. Each servce s assumed to have a cosstecy factor () whch s the probablty that t could delver a requested servce. The reaso for falure to delver a servce may be due to hardware, etwork or system overload problems ad may be dffcult to correct for cost ad other reasos. Our trust broker desg s to detfy the cosstecy factor, or reputato, of each servce. After each trasacto, a user A seds a ratg o a servce B to A s broker. The curret system assumes users are dlget provdg hoest feedbacks. Thus a broker wll collect the complete ad accurate ratgs geerated by ts clets. I ths way, a broker may accumulate eough reputato formato (.e. drect trust) about a servce to support ts clets. However, f a broker fds that ts reputato database s suffcet for makg a Page 66

4 Joural of Electroc Commerce Research, VOL 7, NO.3, 26 recommedato to ts users, t wll cotact the other brokers (.e. formato from coected trust) or reputato authortes (formato from sttuto trust) to gather more formato. Reputato Authorty Reputato Authorty Broker Broker User User User User Fgure 2: System Model Whle users rely o ther brokers to maage reputato formato, brokers talk to each other to sum up the reputato about a subject server. I our model, brokers may decde ot to share certa reputato formato wth aother broker, but they caot le or produce false formato. A server, however, may ot provde cosstet servces or results. Therefore, some server may have a less-tha-perfect reputato. Reputato authorty s the last resort for ay broker f t caot fd suffcet formato about a peer. Reputato authorty, whch may be set up by dustral cosortum or by publc sttutos, matas a global database about all servers. Due to ts sze, however, the ratgs kept by ay authorty may be complete or out of date. For ay gve servce, the ratg from each reputato authorty may be dfferet, just lke credt ratg compaes may have erroeous formato lfe. Brokers therefore wll cosult a reputato authorty oly f there s o other opto avalable. 4. Broker Archtecture A broker has two ma compoets (Fgure 3), reputato maager ad coecto maager. The reputato maager receves requests from clet users ad other brokers. It decdes whether to ask the coecto maager for collectg formato from other brokers or reputato authortes. The coecto maager takes requests from reputato maager ad passes the requests to other brokers ad reputato authortes. I ths secto, we preset each compoet detals. Users Other Brokers Coecto Maagers Reputato Maager Broker Repu DB Coecto Maager Trust DB Other Brokers Reputato Maagers Reputato Authortes Fgure 3: Broker Archtecture Page 67

5 L et al.: A Dstrbuted Reputato Broker Framework for Web Servce Applcatos 4.. Reputato Maager Reputato maager has three fuctoaltes. Frst, t receves ad respods to requests from ts clet users ad other brokers. Secod, t s resposble for tegratg ad matag ts users feedback formato. Thrd, t forwards reputato-solct requests to the coecto maager whe ecessary. Every tme a broker receves a feedback ratg from oe of ts users, t updates the reputato formato about the servce ts reputato database. Each reputato record has the followg felds:. UserID: the ID of the servce 2. Ratg: a reputato value betwee ad 3. Cout: the umber of trasactos used as the bass to geerate the reputato 4. Tmestamp: the tme of the last feedback The tmestamp, whch records the tme whe a feedback ratg was last submtted, s ecessary order to value more recet ratgs wth hgher weghts. The total umber of trasactos used to geerate the ratg s a mportat dcato o the accuracy of the reputato User Request A user seds a trasacto request ad a threshold to ts broker the form of <myid, serverid, reputras>. The broker frst checks the total trasacto cout ts database, ad returs the server s ratg f the cout s greater tha reputras. Otherwse, the broker wll forward the request message to the coecto maager, whch uses the broker-broker protocol to cotact other brokers for reputato data User Ratg The reputato maager collects feedback ratgs from ts clets about each trasacto. Let N be the trasacto cout of the curret ratg Rold. After a clet submts a ratg r regardg x, the reputato maager updates ts reputato value of peer x stored ts database from Rold to Rew. The reputato value of x s updated as follows: βδt N βδt N Rew = e Rold + ( e ) r () N + N + The dfferece feedback tme betwee r ad R old Rold s deoted by Δ t, ad specfes the dscout factor of. Eq. cosders that more recet feedbacks provde more accurate ratgs o the servce s behavor. Ths s true f some servces are subject to recet server hardware problems, etwork coecto problems, or software upgrade ssues. I geeral, the result from more recet trasactos allows users to have a better expectato o the curret servce performace. O the other had, f servce qualty s ot tme depedet we could smply set β as. N I that case, the o-tme depedet formula s smply Rew = Rold + ( ) r that takes the average of all N + N + past ratgs Coecto Maager The coecto maager provdes two mportat fuctos. It matas a lst of trusted brokers as well as a lst of trusted reputato authortes. It acts as the terface betwee the broker ad the trust etwork, ad s resposble for sedg requests to other brokers ad reputato authortes Broker-Broker Trust Protocol I a dstrbuted system, brokers should ot rely oly o ther ow expereces to rate all servces. Collaborato amog brokers s extremely useful. Oly through collaborato ca the system detfy utrustworthy servers promptly ad reduce the rsk for ts clets. For ths reaso, brokers have a strog motvato to cooperate. However, gve that the objectve of a broker s to provde a uque servce to ts users, some brokers may choose ot to share ts data wth other brokers for fear of competto for clets. Therefore, each broker matas a lst of trusted brokers ad ther trust values ts trust database. Trust formato s ot statc. The trust value for a peer broker s based o the umber of accurate recommedatos that peer has provded earler. It s updated each tme after a recommedato s receved ad compared wth the actual trasacto outcome. At the begg, all peer brokers are gve a eutral trust value of X=.5. After each trasacto experece, f the recommedato from the peer s correct, the trust value s updated usg the formula: X = X + F ( X ) (2) Otherwse, X s updated usg the formula X = X ( F) (3) e βδt Page 68

6 Joural of Electroc Commerce Research, VOL 7, NO.3, 26 I the above equatos, F s a postve dex wth a value less tha. For example, f F s.2 ad X was.6, the ew value of X s.68 whe the recommedato receved s good; whle a bad recommedato wll reduce X to a value of.4. The update equatos are desged such a way that X always has a value betwee ad. Moreover, t s more dffcult to ga addtoal trust tha to lose trust whe X has a large value. The coecto maager of each broker matas a lst of fellow brokers sorted by ther trust values. Whe the reputato about a specfc peer s requested, the broker wll cotact the frst m brokers wth a trust value hgher tha a threshold value T. m s called the fa-out value. I addto, a depth parameter may be specfed the recommedato request. A trusted broker wll forward the request to ts ow trusted brokers wth the depth value decremeted by. The forwardg requests form a recursve recommedato cha utl the depth value reaches. The legth of the cha s bouded by the depth parameter, whch s tur decded by how much reputato s already there, specfed by the orgal requestor. For example, suppose that the reputato database curretly has a sze of trasactos, but the user clet wats to have a sze of trasactos, the broker may wat to defe m = 3 ad depth = 2. A total of 2 brokers wll be cotacted by the request (3 the frst level ad 9 the secod level) that most lkely wll retur recommedatos based o the experece of 2 trasactos. If there are already a large umber of trasactos, the broker should use a smaller depth wth a larger m such that the recommedato collecto ca be doe more effcetly wth fewer drect requests. A threshold value T s used to flter out ay low-trust broker from the reputato solctato. The threshold value should be used by all coected brokers at all levels for the specfc request Aggregatg Reputato Recommedatos A coecto maager keeps all recet recommedatos from the broker cha ts database. All recommedatos receved for the same request share the same request ID. The broker uses the followg method to aggregate the recommedatos. Each recommedato R s weghed by the umber of trasactos N, the tme dfferetal factor F( Δ t ), ad the trust value o that broker X. Each broker uses a dfferetal threshold to decde whether the recommedato should be take at the full value. If R was reported wth a tme dfferetal less tha the threshold, the value of the tme dfferetal factor F( ) s ; otherwse, t s β. We have R = N X Δt N R N X N = where *F( ). * F( Δt ) (4) Δt e Δt The coecto maager forwards the reputato recommedato to the reputato maager, whch tur forwards t to the user. I the recommedato, the ly recorded reputato s combed wth the recommedatos receved from all brokers. If the user decdes to act o the recommedato, t wll sed a feedback report to ts broker after the trasacto. The broker s reputato maager wll forward the user s ratg to the recommedato maager. The coecto maager checks ts recommedato database for all foreg recommedatos of the target server. If the user s ratg s the same as the recommedato wth a acceptable marg, the coecto maager wll update the trust value for the broker who set the recommedato. The coecto maager the rearrages the order of the trusted brokers lst accordg to the ew trust value Respodg to Other Brokers As we have dscussed earler, trusted brokers wll cooperate wth each other the commuty. However, f broker B asks C, whch has a small trust value o B, for a recommedato o some servce, C may decde ot to comply wllgly. Ths s oly far f B has ot gve too much credble formato to C the past. The behavor of a broker o aother broker s affected by ther mutual trust values. A broker wll retur the recommedato wth the sze dscouted by the trust value. For example, f the reputato has a sze of trasactos, ad the trust value s.6, the reputato record reported wll reflect oly 6 trasactos. It should be clear that the trust value s ot symmetrc betwee two brokers. Oe broker may be rated hghly by aother broker but ot vce versa. For ths reaso, the trust values for all brokers should be kept by a broker carefully ad prvately. Sce the values are updated dyamcally, the trust relatoshp amog brokers s tmevaryg ad upredctable. However, the log-term relatoshp amog good brokers should preval so that they wll all belog a trusted cluster. I the case whe a broker ad ts trust etwork together do ot have eough evdece about a potetal clet, a broker may cosult a reputato authorty. A reputato authorty collects reputato formato from publc o a volutary bass, ad produces a global ratg o all servces. Sce a reputato authorty s a depedet servce provder, ts data may be more ubased. O the other had, as the case of ay bg orgazato, the data from a global reputato authorty may ot be as accurate ad tmely as some user groups. Page 69

7 L et al.: A Dstrbuted Reputato Broker Framework for Web Servce Applcatos 5. Base Model Performace Study To test the effcecy of our trust formato ad system desg, we have coducted smulato o reputato collectos. Each servce our smulato has a radomly assged cosstecy factor () that defes ts capablty to delver a servce cosstetly. To evaluate the system s reputato kowledge about a servce, we compute the total stadard devato betwee all brokers values o the servce ad the servce s true value. The average devato of all servers defes the system s overall reputato correctess (SC). A system s sad to have perfect reputato kowledge whe SC s. 5.. System Parameters We have coducted smulatos of a reputato system wth 6 users usg 6 brokers. Each broker collects trasacto feedbacks from ts users ad teracts wth other brokers to gather global reputato about other users. I our smulato, the system geerates a ew trasacto every msec. Therefore the same trasacto par (A, B) wll be geerated every 36 secods o average. Sce each broker s matag reputato database for users, we should have oe ew reputato ratg o B receved by A s broker every 36 secods. We decde that ay of B s executo hstory outsde of the wdow of the last trasactos wth A s o loger meagful to A. We ca thus derve the ß value to be /(36* 5 ) or about 2.7* -7 Eqs. () ad (4). I our smulatos, the F value Eqs. (2) ad (3) s radomly selected for each broker the rage of [.2,.5]. Itally, each broker has a trust value of.8 o 4 other brokers, ad.5 o the rest of the brokers. We also set the broker search fa-out m = 2 ad depth = 5. I other words, each broker wll coect to the two brokers trusted most the trust etwork ad the search for reputato data the trust etwork ca go as deep as 5 drect levels. I each of our smulatos, we geerate 6* 6 trasactos betwee users ad servers, ad compute the system correctess after every 6, trasactos ad thus have data pots from each smulato Smulato Result Fgures 4-7 show the smulato results. The x-axs represets the data pots ad the y-axs represets the SC, whch s the average of stadard devato. I each fgure, we have 3 curves, for dfferet tal values o users the broker s reputato database; the values are set to, actual, ad - respectvely. The ß value s set to -6 or 2.7* -7. Aother parameter, the reputato threshold, s used to decde whether to proceed wth the trasacto. If the reputato retur from a broker s below the threshold, A wll ot coduct the trasacto wth B. I that case, o update o B s reputato ca be reported to the broker. The result shows that the tal reputato value has a bg mpact o the system correctess. Whe all brokers have a correct tal value o every user s, they are more lkely to keep the system a reasoably correct state. If the tal reputato data are opposte to the true values (tal values are -) brokers reputato databases, the system wll mprove slowly wth tme, f the reputato threshold s. I all cases, the system correctess s the worst f the tal reputato values are -. If all brokers assume all servces are perfect (=) tally, the SC wll be large tally but mprove sgfcatly over tme. Ths shows our trust etwork s workg ad covergg toward a more formed system. System's overall reputato correctess(sc).6 avgsd *, threshold = Fgure 4: SC for threshold= ad ß= -7 Page 7

8 Joural of Electroc Commerce Research, VOL 7, NO.3, 26 System's overall reputato correctess(sc) avgsd * threshold =.3 Fgure 5: SC for threshold=.3 ad ß= -7 System's overall reputato correctess(sc).6.5 avgsd *, threshold =.5 Fgure 6: SC for threshold=.5 ad ß= -7 System's overall reputato correctess(sc) avgsd *, threshold =.7 Fgure 7: SC for threshold=.7 ad ß= -7 Page 7

9 L et al.: A Dstrbuted Reputato Broker Framework for Web Servce Applcatos Whe the reputato threshold s hgh, a user s more lkely ot to coduct a trasacto wth a supposedly bad server ad thus wll ot be able to geerate ew reputato data to mprove the curret system kowledge, eve whe that kowledge s accurate. Ths s a ssue our curret system desg. We wll eed addtoal mechasms to test f the curret reputato data s correct eve whe the reputato reported s very bad. Ths s lke askg someoe to taste a food tem eve f he kows that most people do ot lke t. There should be some way for the perso to be prepared for the worst, or eve be rewarded for the courage. How to verfy or correct a bad reputato s a terestg problem for our future study. 6. Extedg the Trust Model wth Trasacto Cotext [Dellarocas 23] suggests that trust may ot be a fxed value assocated wth a servce etty but rather s subject to the ettes' behavor ad apples oly wth a cotext at a gve tme. Ettes may vary ther servce qualtes based o the beefts they ca receve. Itellget malcous ettes may adaptvely deploy a strategy order to maxmze ther expected payoff gve the rules of the game. There are a umber of ways whch such peers ca attempt to fool the system ad obta hgher beefts. I e-commerce settgs, f reputato s ot weghed by the trasacto sze (.e. total dollar value), sellers could buld a good reputato by selectvely satsfyg may low-valued trasactos ad the cheatg occasoally o trasactos that volve a hgh value, but stll allows them to mata a acceptable reputato. 6.. Etty s behavor I earler sectos, a servce etty's behavor, whch s represeted by, s assumed to be a costat, o matter how cotext chages. However, ths may ot be true for more sophstcated servces. The probablty of a etty to delver a requested servce successfully may be related to the cotext, such as the beeft t ca get. Therefore, the trust maagemet model should corporate varous cotext values evaluatg the trustworthess of servces dfferet commutes ad trasactos. I ths study, we assume that a seller may try to buld up a good reputato by provdg good servces for small-sze trasactos ad the commt fraud o large-sze trasactos to make a bg proft. We therefore suggest that cotext formato such as trasacto sze must be take to accout. We assume that a seller s servce qualty cotuously degrades as the trasacto sze creases ad the servce qualty does ot vary as tme vares. The cosstecy factor of such sellers s show Fgure 8 as a egatve expoetal dstrbuto fucto whch satsfes the features of that () etty s value decreases gradually as the cotext weght creases ad (2) the rage of s [, α ], where < α. Suppose TB s the sze of the trasacto, (TB), the probablty to delver a requested servce wth a trasacto sze TB, s defed as ( TB) = α exp( β TB), where < α, β > (5) where α s the parameter to descrbe the maxmum value the servce ca acheve ad β s the parameter of the speed that decreases as TB creases. The greater the β s, the faster the value decreases as thetb creases. Etty's value as Cotext Chages Cosstecy Facto.5 Co text Weght (e.g., tras acto s ze) Fgure 8: Etty s value ad cotext weght I our desg, each reputato record cludes the followg felds: UserID: ID of the server The maxmum ad mmum values of trasacto sze Page 72

10 Joural of Electroc Commerce Research, VOL 7, NO.3, 26 α, β :the two parameters to defe the fucto (Eq. 5) whch specfes the etty s dyamc behavor. The ratg of a etty at certa TB value ca be obtaed oce α, β are kow. Cout: umber of trasactos used to geerate α, β 6.2. User s Reputato Iqury Smlar to the system desg the base model, a user seds a trasacto request ad a threshold to ts broker the form of <myid, serverid, reputras, TB>. TB s the ew parameter whch s the value of the trasacto sze. The broker frst checks the total trasacto cout ts database, ad returs the server s ratg f the sze s greater tha reputras. Otherwse, the broker forwards the request message to the coecto maager, whch wll use the broker-broker protocol to request other brokers for reputato data. I the exteded model, the approach to aggregate the recommedatos s smlar to Eq. (4) except that the tme factor s removed. Ths s because ths ew model, we assume R s depedet of tme. Therefore, each recommedato R s weghted by the umber of trasactos N, ad the trust value o that broker X. Each broker uses a dfferetal threshold to decde whether a recommedato should be take at the full value. We have X N R R = N where N X N = 6.3. Reputato Update The Least Squares Fttg Expoetal techque The ultmate goal of reputato update s to make the α, β values to be as close as to α, β as possble. We use the Least Squares Fttg Expoetal techque to derve α, β as users feedbacks are accumulated. Suppose ( x, y ),( x, y )...( x, y 2 2 ) are the sample pots that st o the curve y = α exp( β x). To ft ths curve, suppose α exp(a) ad β B, the best-ft values of A ad B are: A B 2 ( x y ) ( y l y ) ( x y ) = = = = = 2 2 y ( x y ) ( x y ) = = = y ( x y l y) ( x y) = = = = = 2 2 y ( x y ) ( x y ) = = = ( x y l y ) ( y l y ) (6) (7) (8) Every tme a user submts a feedback, the Least Squares Fttg Expoetal techque s used to amed α, β. The more feedbacks users submt, the closer β α, wll be close to α, β, the actual parameters of the etty Reputato Update Procedures Every tme a broker receves a feedback ratg from oe of ts users, t updates the reputato formato about the server ts reputato database. After a clet submts a ratg r regardg etty x wth the trasacto sze tb, the reputato maager updates ts reputato value of peer x stored ts database from R to R. Assume Rold s the old ratg for the trasacto sze tb. α, β are the latest approxmatos of α, β that are saved the broker. N s the trasacto cout of the curret reputato ratgα, β. The R old s calculated by Rold = α exp( β tb), ad the reputato value for trasacto sze tb s N updated usg the formula: Rew = Rold + r. ( tb, Rew) s the ew data pot to be used by the Least N + N + Squares Fttg process to produce ew α, values. β old ew Page 73

11 L et al.: A Dstrbuted Reputato Broker Framework for Web Servce Applcatos Theoretcally, the best way to perform Least Squares Fttg s to save all hstorcal sample data pots the database. However, from the egeerg perspectve ths approach wll cause a large memory overhead. Oce users ad feedbacks crease, ths overhead could be very expesve for the server whch s rug the trust maagemet broker program. Therefore, a alteratve soluto s adopted our soluto wthout savg actual sample data pots the database. I our approach, the total rage of the cotext s equally dvded to fxed tervals, as show Fgure 9. For each terval, oly the boudares (the coordates of start pot ad ed pot) ad the cout of the terval (.e., how may feedbacks have be submtted wth the cotext fallg that terval) are saved. We calculate the terval data pot ( x, y) for each terval for fttg process as follows. For a terval that has receved just oe feedback, x, y) = ( tb, R ) ; for other tervals, x, y) = ( x avg, y ), where x avg, y ) s a pot o the fucto ( ew ( avg ( avg Cosstecy Factor E tty's C F value as Cotext Chages * * I te rva l D a ta P o t * I te rv a l I te rv a l 2... * * * I te rv a l C otext W eght(e.g., trasacto sze) Fgure 9: Expoetal Least Squares Fttg usg sample data pots y = α exp( β x). y s the average value of the fucto y = α exp( β x) o the tb tb2 terval rage from to, ad ca be foud as follows: avg y avg = tb2 α exp( β tb Iterval Gve y, x ca be calculated sce x avg, y ) s a pot o the egatve expoetal curve. avg avg ( avg Durg the fttg process, each terval data pot s duplcated by the cout of that terval order to gve each terval data pot a dfferet mportace weght. Fally, f there are tervals, a total of Cout + Cout Cout data pots wll be used for the fttg process based o Eqs. (7) ad (8). 2 legth x) 7. Performace Study for System wth Trasacto Cotext To model the ettes cotext depedet behavor, every etty a system has a radomly assged α, par that defes ts dyamc capablty to delver a servce. The probablty of the etty to delver ts β servce at trasacto tb s defed as y = α exp( β tb). Every broker keeps ts reputato database α, β of all servers. β α, are geerated from users ratg reports. To evaluate the system s reputato kowledge about a server, we compute the tegral of square values betwee all brokers the server ad α, β. Assume there are m users ad brokers total, the square tegral s: α, β o Page 74

12 Joural of Electroc Commerce Research, VOL 7, NO.3, 26 Cotext max 2 ( a exp( β x) a exp( β x)) Cotext l = SI = m The average square root of square tegral of all servers defes the system s overall reputato correctess (SC) where SC = m = SI. A system s sad to have perfect reputato kowledge whe SC s. 7.. System Parameters All of the smulato parameters, except the tal value ( value assged ( I ths smulato, α, β ) the broker, rema the same as Secto 5.. α s assged a radom value from the set {.,.2...} α, β ) assged to the etty ad the tal β, s radomly selected from the set..2.,.... I the broker, the tal ( α, β ) s set to Cotextmax Cotextmax Cotextmax β α, β ), ad (, ) for three testg cases of tal broker s kowledge of actual, - α, ), ( (, ad respectvely. The mmum cotext s set to ad the maxmum cotext s set to,. The rage of the cotext s equally dvded to 5 tervals for the least square fttg process Smulato Result Fgures -3 show the results from our smulatos. The x-axs represets the data pots ad the y-axs represets the SC, whch s the average of stadard devato. Each fgure has 3 curves, usg dfferet tal values o users the broker s reputato database; the values are set to, actual, ad - respectvely. Aother parameter, the reputato threshold, s used to decde whether to proceed wth the trasacto. If the reputato retur from a broker s below the threshold, A wll ot coduct the trasacto wth B. I that case, o update o B s reputato ca be reported to the broker. System's overall reputato correctess (SC) D AvgS *, threshold = Fgure : SC for repu threshold= All of the smulato results show that the SC curves the exteded model follow the same tred as the base model: the hgher the threshold s set, the slower the AvgSD decreases. Ths matches the statstcal ature of reputato that the more feedbacks are accumulated, the more accurate the smulated parameters approach the actual servce behavor. At the same tme, we observe o sgfcato smulato performace degradato the exteded servce model eve whe the dyamc servce behavors eed to be deal wth the presece of cotext chage. Ths demostrates the effcecy of our Expoetal Least Squares Fttg approach. It does ot save ay actual sample data pots the database ad stll ca effectvely evaluate the dyamc servce model s parameters wth reasoable costs. Page 75

13 L et al.: A Dstrbuted Reputato Broker Framework for Web Servce Applcatos System's overall reputato correctess (SC) AvgSD *, threshold =.3 Fgure : SC for repu threshold=.3 System's overall reputato correctess (SC) AvgSD *, threshold =.5 Fgure 2: SC for repu threshold=.5 System's overall reputato correctess (SC) 6 5 AvgSD *, threshold =.7 Fgure 3: SC for repu threshold=.7 Page 76

14 Joural of Electroc Commerce Research, VOL 7, NO.3, Coclusos Wth the expaso of ole servces ad the growg adopto of Web servces stadards, we expect a cotug growth of e-servces ad e-commerce. Due to the urelable ature of e-servces, t s mportat to check the trustworthess of ay servce before t s voked. However, most e-servce users may ot be capable or eve bother to deploy a trust measure themselves. O the other had, they may be coected to a trust commuty that ca provde them wth valuable expereces o a potetal servce. It s both effectve ad effcet for a user to use ad to share reputato formato such a fredly dstrbuted trust etwork. I ths paper, we have preseted a dstrbuted trust framework where servce brokers maage trust formato for users. A broker keeps a trust value o each of ts fellow brokers the etwork ad updates the trust value after checkg ther recommedato agast the actual experece. A broker also matas the reputato o e-servers usg the feedback from clets ad from other brokers. Our smulato shows that ths s a effectve way to maage trust ad reputato the e-servce evromet. We pla to cotue ths study the future, explorg further the trust ad reputato maagemet a more complcated cotext evromet that both the chages of tme ad trasacto sze ca trgger the chage of servce behavor, as well as more effectve mechasms to elmate the mpact of dshoest feedbacks. REFERENCES Atallah, M.J., H.G. Elmogu, V. Deshpade, ad L.B. Schwartz, Secure Supply-cha Protocols, Proc. of IEEE Cof. o E-Commerce, pp , Newport Beach, CA, Jue 23. Che, H., Yu, T., ad K.J. L, QCWS: A Implemetato of QoS-Capable Multmeda Web Servces. Proceedgs of the 5th It. Symposum o Multmeda Software Egeerg, pp , Tachug, Tawa, Dec 23. Che M., A.N.K. Che, ad B.B.M. Shao, The Implcatos ad Impacts of Web Servces to Electroc Commerce Research ad Practces, Joural of Electroc Commerce Research, volume 4, umber 4, 23 Che M., ad M.J. Mexell, Web Servces Eabled Procuremet the Exteded Eterprse: A Archtectural Desg ad Implemetato, Joural of Electroc Commerce Research, volume 4, umber 4, 23 Dellarocas, C. ad P. Resck, Ole Reputato Mechasms: A Roadmap for Future Research Summary report of the Frst Iterdscplary Symposum o Ole Reputato Mechasms, Aprl 26-27, Dellarocas, C., Buldg Trust O-Le: The Desg of Robust Reputato Mechasms for Ole Tradg Commutes. Chapter VII, Iformato Socety or Iformato Ecoomy? A combed perspectve o the dgtal era, Doukds, G., Myloopoulos, N. ad Pouloud, N. (Eds.), Idea Group Publshg, 24 Jurca, R. ad B. Faltgs, A Icetve Compatble Reputato Mechasm, Proc. of IEEE Cof. o E-Commerce, pp , Newport Beach, CA, Jue 23. Lberty Allace, 24 Wag Y.S., T.I. Tag, ad J.T.E. Tag, A Istrumet for Measurg Customer Satsfacto Toward Web Stes That Market Dgtal Products ad Servces, Joural of Electroc Commerce Research, volume 2, umber 3, 2 Xog, L. ad L. Lu, PeerTrust: Supportg Reputato-Based Trust for Peer-to-Peer Electroc Commutes, IEEE Trasactos o Kowledge ad Data Egeerg, Specal Issue o Peer to Peer Based Data Maagemet, Vol. 6, No. 7, July 24. Yu, B. ad M. Sgh, A Socal Mechasm of Reputato Maagemet Electroc Commutes, Proc. of 4 th It. Workshop o Cooperatve Iformato Agets, pp , 2. Yu, T. ad K.J. L, Servce Selecto Algorthms for Web Servces wth Ed-to-ed QoS Costrats Proc. of IEEE Coferece o E-Commerce Techology, Sa Dego, CA, July 24. Page 77