Leveraging Social Networks to Combat Collusion in ...hs6ms/publishedPaper/Journal/2012/0622636… · Overstock and found that buyer purchasing and rating behaviors are greatly affected

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Leveraging Social Networks to Combat Collusion in ReputationSystems for Peer-to-Peer Networks

Ze Li, Student Member, IEEE, Haiying Shen*, Member, IEEE , Karan Sapra

Abstract—In peer-to-peer networks (P2Ps), many autonomous peers without preexisting trust relationships share resources with eachother. Due to their open environment, the P2Ps usually employ reputation systems to provide guidance in selecting trustworthy resourceproviders for high reliability and security; however node collusion impairs the effectiveness of reputation systems in trustworthy nodeselection. Although some reputation systems have certain mechanisms to counter collusion, the effectiveness of the mechanisms isnot sufficiently high. In this paper, we leverage social networks to enhance the capability of reputation systems in combating collusion.We first analyzed real trace of the reputation system in the Overstock online auction platform which incorporates a social network.The analysis reveals the impact of the social network on user purchasing and reputation rating patterns. We thus identified suspiciouscollusion behavior patterns and propose a social network based mechanism, SocialTrust, to counter collusion. SocialTrust adaptivelyadjusts the weight of ratings based on the social distance and interest relationship between peers. Experiment results show thatSocialTrust can significantly strengthen the capability of current reputation systems in combating collusion.

Index Terms—Peer to peer networks, Reputation systems, Collusion, Social networks.

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1 INTRODUCTION

The past decade has seen a rapid development of peer-to-peer networks (P2Ps) along with a dramatic surge ofapplications including file sharing (e.g., BitTorrent [1]),video streaming (e.g., PPLive [2]), and computing re-source sharing (e.g., MAAN [3]). In these P2P appli-cations, peers (acquaintance and non-acquaintance) di-rectly contact each other to conduct transactions onresources (e.g., files, videos and etc.). Considering P2Ps’open environment where many autonomous nodes shareresources, a critical problem is how a resource requestercan choose a resource provider that is trustworthy andprovides high-quality of service (QoS).

To deal with this problem, P2Ps usually employ rep-utation systems for reliability and security. Like thereputation systems in eBay [4], Amazon [5] and Over-stock [6], a reputation system employed in P2Ps com-putes and publishes global reputation value for eachnode based on a collection of local ratings from otherusers in order to provide guidance in selecting trust-worthy nodes; however reputation systems are generallyvulnerable to node collusion [7], [8], which impairstheir effectiveness in trustworthy service selection. Acolluding collective is a group of malicious peers whoknow each other, give each other high ratings, and giveall other peers low ratings in an attempt to subvert thesystem and gain high global reputation values [9].

A number of reputation systems employ certain mech-anisms to fight against collusion. Although the mech-anisms can reduce the influence of collusion on rep-utations to a certain extent, they are not sufficientlyeffective in countering collusion, or they contradict theP2Ps’ goal of global resource sharing. Some mechanismsassign higher weights to ratings from pretrusted peers

• * Corresponding Author. Email: [email protected]; Phone: (864) 6565931; Fax: (864) 656 5910.

• The authors are with the Department of Electrical and Computer Engi-neering, Clemson University, Clemson, SC, 29634.E-mail: {zel, shenh, ksapra}@clemson.edu

and (or) assign weights to ratings according to the raters’global reputations [10]–[12]. However, colluders can rateeach other in a high frequency or compromise pretrustedpeers to quickly raise their reputations. In other mech-anisms, a peer evaluates others’ trustworthiness basedon the experience [13]–[15] of itself or its friends [16].However, these mechanisms limit the service optionsand prevent strangers from freely conducting transac-tions between each other.

Due to the soaring popularity of the online socialnetwork (e.g., Facebook), more and more applications(e.g. Overstock [6], Oneswarm [17]) incorporate onlinesocial networks into their services to increase userinteractions. In this paper, we propose a mechanismcalled SocialTrust that leverages social networks toenhance the effectiveness of current mechanisms incombating collusion in P2P networks. SocialTrust worksfor a P2P network integrated with an online socialnetwork (already exists or newly constructed) such asthe Maze file sharing system [7]. In a reputation system,after a client’s resource/service request is resolved by aserver, the client rates the service quality of the server.The reputation system collects all ratings of a nodeand calculates its global reputation value, which isused to guide subsequent server selection. SocialTrustis built upon the reputation system of the P2P networkand re-scales node reputation values based on usersocial information to mitigate the adverse influence ofcollusion. To investigate the impact of a social networkon user purchasing and rating patterns and define thesuspicious behaviors in P2P network, we analyzed areal trace of 450,000 transaction ratings during 2008-2010that we crawled from Overstock Auctions (Overstockin short) [6]. Overstock is an online auction platformsimilar to eBay, but it distinguishes itself by integratinga social network into the market community. We foundthat social closeness and interest similarity impact userpurchasing and rating patterns. First, users tend to buyproducts from high-reputed users or socially-close (3hops or less) users. They also rate socially-close userswith high ratings. Second, 88% of a user’s purchasesis within 20% of the user’s product interest categories

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IEEE TRANSACTIONS ON COMPUTERSThis article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.

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on average, and 60% of transactions are conductedbetween users sharing >30% interest similarity.

The observations on the purchasing transactions inOverstock can be directly mapped to resource trans-actions in P2P applications, in which a peer selects aserver for a resource/service request. Based on our ob-servations, we identified suspicious collusion behaviorpatterns based on the distance and interest relationshipbetween peers in a social network. SocialTrust adjuststhe ratings of suspicious colluders according to the socialcloseness and interest similarities of nodes in order toreduce the impact of collusion. By preventing colludersfrom gaining profits (e.g., reputations value) throughcollusion, the colluders underlying business model willbe destroyed. Then nodes do not have incentives tocollude with each other.

This work is the first that leverages a social network toidentify suspicious collusion behavior patterns and re-duce the influence of collusion on reputation systems. Insummary, this work makes the contributions below:(1) We crawled and analyzed user transaction trace fromOverstock and found that buyer purchasing and ratingbehaviors are greatly affected by the distance and in-terest similarity of users in the social network and byseller reputation. Accordingly, we identified a numberof suspicious collusion behavior patterns.(2) We propose the SocialTrust mechanism to enhance areputation system’s capability in countering suspiciouscollusion behaviors learned from the trace data. Social-Trust adjusts the ratings from suspected colluders basedon rater-ratee social closeness and interest similarity.(3) We conducted extensive experiments to evaluate So-cialTrust’s effectiveness in handling different types ofcollusions. The experimental results show that currentreputation systems are not sufficiently effective in deal-ing with collusion, and SocialTrust can significantly en-hance their capability to effectively counter collusion.

The remainder of this paper is as follow. Section 2introduces related works in reputation systems and incollusion deterrence. Section 3 presents our investigationon the real trace. Section 4 describes SocialTrust indetail. Section 5 presents the performance evaluation ofSocialTrust. Section 6 concludes the paper with remarkson our future work.2 RELATED WORKReputation systems In recent years, many reputa-tion systems [10], [12], [18]–[27] have been proposed.PeerTrust [18] computes peer reputation scores based onthree basic trust parameters and two adaptive factors.Trustme [19] offers an approach toward anonymous trustmanagement, which can provide mutual anonymity forboth the trust host and the trust querying peer. Eigen-Trust [10] and PowerTrust [20] depend on the distributedhash tables to collect reputation ratings and calculatethe global reputation value of each peer. TrustGuard [12]incorporates historical reputations and behavioral fluc-tuations of nodes into the estimation of their trustworthi-ness. FuzzyTrust [21] uses fuzzy logic inferences to betterhandle uncertainty, fuzziness, and incomplete informa-tion in peer trust reports. GossipTrust [22] enables peersto share weighted local trust scores with randomly se-lected neighbors until reaching global consensus on peerreputations. Scrubber [23] fights polluted file content

by rating both the file provider and file. Credence [24]gives users a robust estimate of file authenticity (thedegree to which an object’s content matches its ad-vertised description). Fabrizio et al. [25] proposed anapproach to P2P security that enables each client tocompute a personalized, rather than global, performancescore for peers, and also distinguish peer performancefrom peer credibility. Both XRep [26] and X2Rep [27]extend the work in [25] by additionally computing objectreputations based on weighted peer voting.

Collusion deterrence Recently, a number of researchworks have been conducted on the problem of collusionin reputation systems. EigenTrust [10] breaks collusioncollectives by assigning higher weight to the feedback ofpretrusted peers. Moreton et al. [28] proposed the Stampalgorithm, where peers issue stamps as virtual currencyfor each interaction, and the value of each peer’s stampsis maintained by exchange rates that act as reputationvalues. TrustGuard [12] gives more weight to the feed-backs from similar ratings, acting as an effective defenseagainst potential collusive nodes that only give goodratings within the clique and give bad rating to everyoneelse. Qiao et al. [7] analyzed the traffic logs in a P2Pfile sharing system to study different types of collusionpatterns. Mao et al. [11] introduced using social networksin the Maze P2P file sharing system to reduce the impactof collusion. The authors assumed that the pretrustedpeers only trust their friends, and proved that the friendnetwork of the pretrusted peers can help to detect col-luders. The works in [13]–[15] let a peer evaluate others’trustworthiness based on its experience. Sorcery [16] letsclients utilize the overlapping voting histories of boththeir friends and the content providers to judge whethera content provider is a colluder. However, the socialnetwork based method limits the service options andconstrains resource sharing to only between friends. Italso cannot provide a global reputation of each nodecalculated by ratings from a variety of users to accu-rately reflect its trustworthiness. Our proposed methodis the first that leverages social distance and interestrelationship from a social network to identify suspiciouscollusion and to reduce its influence on node reputation.

Sybil attack deterrence Collusion shares similarity toSybil attacks in the sense of forming a collective to gainfraudulent benefits. Thus, we also include a number ofsocial network based works on Sybil attacks as relatedworks. Since malicious users can create many identitiesbut few trust relationships, there is a disproportionately-small “cut” in the graph between the Sybil nodes andthe honest nodes. SybilGuard [29] exploits this propertyto bound the number of identities a malicious user cancreate. SybilLimit [30] improves SybilGuard by lever-aging multiple independent instances of the randomroute protocol to perform many short random routesand exploiting intersections on edges instead of nodes.SybilInfer [31] builds a probabilistic model of honestsocial networks and a Bayesian inference engine thatreturns potential regions of dishonest nodes. SumUp [32]prevents Sybil attack by comparing the social structureof colluders with that of non-colluders. Viswanath andPost [33] compared the existing designs of the Sybildefense schemes and showed that some communitydetection algorithms can defend against Sybil attacks.


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Fig. 1: Effect of reputation on transaction.

They also demonstrated that a well-defined communitystructure is inherently more vulnerable to Sybil attacks.Lesniewski-Laas et al. [34], [35] proposed a Sybil-resilientdistributed hash table routing protocol to reduce theprobability of routing collusion. These schemes can beused to complement SocialTrust to strengthen its capa-bility to detect collusion behaviors.

3 ANALYSIS OF REAL TRACE IN OVERSTOCK

In order to study the relationship between a user so-cial network, transaction, and reputation system, weanalyzed our crawled data of 450,000 transactions be-tween over 200,000 users from Sep. 1, 2008 to Sep. 1,2010 in Overstock. Overstock is an online e-commercewebsite that provides an online auction platform to alarge community of users worldwide to conduct P2P e-commerce. A buyer and a seller on Overstock rate eachother after a transaction, and the ratings are aggregatedto form a user’s global reputation. The range of ratings inOverstock is [-2,+2]. Each user has a “personal network”and a “business network.” The “personal network” isa social network that is comprised of users connectedby friendship links. If a user accepts the friend invita-tion from another user, a friendship link is establishedbetween the two users in the social network. A usercan list hobbies and interests, post photos, and publishfriend and business contact lists in his/her personalpage in the personal network. The “business network”records the user’s business contact list. Every time aftera user completes a transaction, (s)he adds the transactionpartner into his/her business network.

To crawl the data, we first selected a user in theOverstock as a seed node, and then used the breadthfirst search method to search through each node in thefriend list in the personal network and business contactlist in the business network. Based on the trace data, wetry to identify suspicious collusion behavior patternsbased on two main characteristics of collusion describedin [7], [10]. First, colluders are normally socially-closenodes. Second, colluders frequently rate each other withhigh values in order to boost the reputation values ofeach other and (or) give others low values in order tosuppress their reputation values and gain benefits.

3.1 Relationship between reputation, social networkand transactionSince users usually refer to sellers’ reputations for sellerselection, we first investigated the relationship betweena user’s reputation and the number of users in theuser’s business network. Figure 1(a) shows that thereis a linear relationship between the reputation value ofa user and the size of the user’s business network. The

strength of the linear association between two variables,x and y, can be quantified by the correlation coefficient,C = s2xy/sxxsyy, where sxy =

∑(xi− x)(yi− y), sxx =

∑(xi− x)2

and syy =∑

(yi − y)2. The correlation coefficient betweenthe reputation value and business network size is 0.996.Since users prefer to buy products from trustworthyusers, sellers with higher reputations are more likelyto attract more buyers, hence have larger business net-works. This is confirmed by Figure 1(b), which showsthe number of transactions a user has received is pro-portional to his/her reputation. This means that userswith higher reputations attract more transactions. Thisis also the motivation of colluders to conspire togetherto boost the reputation of each other. Thus, we make anobservation (O) from the results:O1: Users with higher reputation values are more likelyto attract more buyers, and users seldom buy productsfrom low-reputed sellers.We then derive an inference (I) from O1.I1: A buyer is unlikely to frequently rate a low-reputeduser with high or low ratings, since (s)he is unlikely torepeatedly choose a seller with low QoS.

Figure 2 shows the number of users in the personalnetwork of each user versus her/his reputation value.We can see that there is a very weak linear relationship

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Fig. 2: Social network size vs.reputation (C=0.092)

between personal networksize and reputation value.Their correlation coefficientis only 0.092. The linearrelationship may be causedby the reason that a high-reputed user knows manyusers from his/her largebusiness network, whomay become the user’sfriends. The weak linear relationship implies that alow-reputed user may have the same personal networksize as a high-reputed user.O2: A low-reputed user may have a large number offriends in his/her social network.I2: A low-reputed user may have many socially-closefriends that (s)he can collude with in order to increasehis/her reputation.

3.2 Impact of social closenessSocial distance between two nodes is the number ofhops in the shortest path between them in the personalnetwork, which represents the social closeness betweenthe two users. If two users are directly connected inthe personal network, their social distance is 1. Next,we investigate the impact of social distance on userpurchasing and reputation rating behavior.

Figures 3(a) and (b) show the average rating valuesand average number of ratings from buyers to sellerswith different social distances in hops ≤ 4, respectively.We see that as the social distance between peopleincreases, the average rating values and averagenumber of ratings decrease.O3: Most transactions with high ratings occur betweenusers within 3 hops.Thus, we identify a suspicious behavior (B) of collusion:B1: Users with long social distances rate each otherwith high ratings and high frequency.


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Fig. 3: Impact of social distance on reputation and transaction.

O4: Users with shorter social distances are more likelyto rate each other with higher ratings and higherfrequency.From I1, I2 and O4, we retrieve:B2: A user frequently rates a low-reputed socially-closeuser with high ratings.

3.3 Impact of social interest similarity

Next, we investigate the impact of user interest on userpurchasing patterns. We classified the products boughtor sold by the users into categories such as Electronics,Computers, and Clothing. We then generated an interestset V=<v1, v2, v3, ..., vk> for each user, where v denotesa product category. We ranked the categories that eachbuyer has purchased from in descending order of thenumber of the products (s)he has purchased in eachcategory. We define the percent of a category rank as theratio of the average number of products in the categoryrank per user over the average number of all productsbought per user. Figure 4(a) plots the Cumulative Dis-tribution Function (CDF) of the percent of each categoryrank. The figure shows that the number of productsin different category ranks conforms to a power lawdistribution. It also shows that the top 3 categories ofproducts constitute about 88% of the total number ofproducts a user bought. Thus,O5: A user mostly buys products in a few categories(≤3) in which (s)he is interested.It was indicated that normal nodes primarily requestitems in their interests [36]. Our above analytical resultsare consistent with this finding. We calculated the inter-est similarity between each pair of buyer ni and sellernj by |Vi ∩ Vj |

min(|Vi|, |Vj |). (1)

Figure 4(b) depicts the CDF of the average number oftransactions versus interest similarity. We see only 10%of transactions are conducted between users with ≤20%interest similarity, 60% of transactions are conductedbetween users with >30% interest similarity, and moretransactions occur between users with interest similarityhigher than 50%.O6: A buyer seldom buys products from sellers with lowinterest similarity.B3: Users with few common-interests rate each otherwith high ratings and high frequency.

Based on O1, I1 and O6, we know that a seller may tryto suppress the reputation of his/her competitors whosell similar products by frequently rating them with lowratings. Thus, we identify another suspicious behavior:B4: A buyer frequently rates a seller with manycommon-interests with low ratings.

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Fig. 4: Impact of interests on purchasing patterns.

4 SOCIALTRUST: SOCIAL NETWORK BASEDMECHANISM TO COMBAT COLLUSION

SocialTrust can be used in any reputation system for P2Pnetworks to enhance its capacity to combat collusion.As most reputation systems for P2P networks [10],[12], [18]–[27], we assume that most of the users inthe network are rational and legitimate nodes. If a P2Pnetwork already has a social network like Overstock andthe Maze file sharing system [7], SocialTrust can directlyuse the social network. Otherwise, SocialTrust providesa plugin for the social network construction. It requiresusers to enter their interest information and establishfriend relationships as in other reputation systems [11],[16], [29]. Like current online social networks (e.g.,Overstock and Facebook), SocialTrust maintains arecord of interactions among users on the personalnetwork. It processes both the interest information andinteraction information for combating collusion.

SocialTrust derives the social closeness (from the socialrelationship and node interaction) and interest similarity(from node profiles or activities) between a pair of nodes.We use Ωc and Ωs to respectively denote these twocoefficients. SocialTrust detects action patterns of suspi-cious collusion behaviors and then reduces the weightof the ratings from suspected colluders based on thetwo coefficients. Note that some non-colluders’ behav-iors might be coincident with our identified suspiciousbehavior patterns. However, such cases are rare since thesuspicious behaviors are abnormal as shown in Section 3.Since collusion from malicious nodes greatly impairs thesystem performance, the benefits from reducing collu-sion’s influence should outweigh the effect of a marginalamount of possible false positives in SocialTrust.

4.1 Social closeness based collusion deterrenceSocial network studies [37], [38] indicate that the numberof social relationships and interaction frequency deter-mine the social closeness of a pair of adjacent nodes,ni and nj . The social relationships (e.g., colleague andclassmate) between a pair of nodes are always indicatedin a social network, and their interaction frequencymeans their online contact frequency. In a P2P networkincorporated with a social network, an interaction canbe regarded as an action that a peer requests a re-source from another peer. More relationships betweentwo nodes mean a closer relationship between them.Also, if ni interacts with nj more frequently than withother friends, it means that ni is socially-closer to nj .Rather than using the complex supervised learning algo-rithms [37], [38] for social closeness modeling, which iscomputationally expensive especially in a large-scale dis-tributed P2P system with dynamic interaction frequency


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and social relationship information updates, SocialTrustintroduces a lightweight social closeness model.

In SocialTrust, considering the two factors, the socialcloseness Ωc(i,j) between two adjacent nodes ni and nj

is calculated byΩc(i,j) =

m(i,j)f(i,j)∑|Si|

k=0 f(i,k), (2)

where m(i,j)≥1 denotes the number of social rela-tionships between ni and nj , f(i,j) denotes the socialinteraction frequency from ni to nj , and Si denotes a setof neighbors of node i, where |Si| denotes the numberof nodes in Si.

Intuitively, for a pair of non-adjacent nodes that rateeach other but have no direct social relationship, fewerhops in the shortest path between the two nodes inthe social network graph means a closer relationship.Since each node establishes its own friend-relationshipnetwork, broadcasting can be used to find the shortestpaths. However, broadcasting generates a large amountof overhead. Binzel et al. [39] indicates that a reductionin social distance between two people significantly in-creases the trust between them. Also, the trace data fromOverstock shows that users normally do business withothers within 3 hops in their personal networks, whichis consistent with the observation in [40] that the userspossessing a social network primarily transact with 2to 3 hop partners. Therefore, the friend-of-friend (FOF)relationship [41] is sufficiently accurate to capture theindirect social closeness between two nodes. Meanwhile,if two nodes have more common friends, they are morelikely to have a close social relationship based on thehomophily feature in social science [42]. Therefore, usingSi and Sj to respectively denote the set of friends oftwo non-adjacent nodes, ni and nj , the social closenessbetween two non-adjacent nodes ni and nj is:

Ωc(i,j) =∑

k∈|Si∩Sj |

Ωc(i,k)+Ωc(k,j)

2(3)

That is, we find all the common friend nk betweennode ni and nj . The social closeness between ni and nj

through nk is calculated by averaging the closeness ofΩ(i,k) and Ω(k,j) if the common friends exsit. Otherwise,the closeness value is the minimum social closenessvalue of neighbor nodes in the social paths between ni

and nj [43]. In summary:

Ωc(i,j) =

⎧⎪⎪⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎪⎪⎩

m(i,j)·f(i,j)∑|Si|

k=0f(i,k)

adjacent nodes,

∑k∈|Si∩Sj |

Ωc(i,k)+Ωc(k,j)

2non-adjacent nodes, k �= ∅

min1≤i≤n

Ωc(ki,ki+1)non-adjacent nodes, k = ∅,

(4)where node ki is in the path between ni and nj .

Suppose F is the average rating frequency from onenode to another node in the system, SocialTrust usesθF (θ > 1) as the threshold to determine whether therating frequency is high, where θ is a scaling parameter.For example, in Overstock, F = 2.2/month. Accordingto B3 and B4 described in Section 3, when ni rates nj

with high ratings and high frequency, if Ωc(i,j) is verylow or very high and nj ’s reputation is low, it means ni

is potentially a colluder. Then, SocialTrust reduces theweight of the ratings from ni to nj based on Ωc(i,j) . For

this purpose, SocialTrust relies on Gaussian function asa reputation filter.

As shown in Figure 5, the Gaussian function hasa characteristic symmetric “bell curve” shape that canmitigate or filter the effect of a factor with values greatlydeviated from the normal value. That is,

f(x) = ae−(x−b)2

2c2 , (5)where parameter a is the height of the curve’s peak, bis the position of the centre of the peak, and c controlsthe width of the “bell”. SocialTrust uses the Gaussianfunction to adjust the ratings from ni to nj , denoted byr(i,j).

r(i,j) = r(i,j) · α · e−(Ωc(i,j)

−Ωci)2

2|maxΩci−minΩci

|2 , (6)where α is the function parameter. maxΩci , minΩci ,

and Ωci respectively denote the maximum, minimum,and average social closenesses of ni to the nodes thatni has rated.

We set α = a to adjust the weight of ratings, b = Ωci ,which is the most reasonable social closeness of ni toother nodes it has rated, and c = |maxΩci − minΩci |,which is the greatest variance of social closeness of ni toother nodes it has rated. The exponent in Equation (6) isthe deviation of Ωc(i,j) from the normal social closenessof ni to other nodes it has rated. We also can replace Ωciwith the average Ωc of a pair of transaction peers in thesystem based on the empirical result.

As Figure 5 shows, the Gaussian function can sig-nificantly reduce the weights of the ratings from thenodes with very high or very low social closeness tothe ratees, mildly reduce the weights of those from thenodes with high or low social closeness to the ratees, andnearly maintain the ratings from the nodes with normalcloseness to the ratees. As a result, the weight from theratings from suspected colluders is reduced.

4.2 Interest similarity based collusion deterrenceIn SocialTrust, each node has an interest setV=<v1, v2, v3, ..., vk> indicating its interests. In P2Papplications, the interests of a peer can be derived fromthe resources it frequently requests or from the interestsin the user’s social network profile. As mentioned, thesocial interest similarity between ni and nj is calculatedby:

Ωs(i,j) =|Vi ∩ Vj |

min(|Vi|, |Vj |) . (7)

Nodes with larger Ωs share more interests.One property of social networks is that nodes with

common interests tend to interact with each other moreoften than with other nodes [42]. This was confirmed inprevious study [44] on peoples’ relations based on theirinterested files. P2P resource sharing and transactionsusually occur between nodes sharing similar interests,As a result, if two nodes ni and nj sharing few interests(i.e., small Ωs(i,j)) rate each other frequently, they arelikely to be colluding with each other as indicated inB3 in Section 3. As indicated in B4, if two nodes havinga high interest similarity but one frequently rates theother with low ratings, they are likely to be businesscompetitors and the rater is a potential malicious node.

In these two cases, SocialTrust reduces the weight ofthe ratings from suspected colluders that have very highor low Ωs(i,j) with the ratee using the Gaussian function:


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Social closeness / Interest similarity

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Social closeness /interest similarityis too high

0

Fig. 5: One-dimensional reputa-tion adjustment.

Fig. 6: Two-dimensional reputa-tion adjustment.

r(i,j) = r(i,j) · α · e−(Ωs(i,j)

−Ωsi)2

2|maxΩsi−minΩsi

|2 , (8)where maxΩsi , minΩsi and Ωsi denote the maximum,minimum and average interest similarity of node ni withthe nodes it has rated, respectively. According to B3and B4, the rating from ni to nj is adjusted accordingto Equation (8) when ni frequently rates nj with highratings and (Ωs(i,j) − Ωsi) < 0 which implies that ni andnj share few interests, or when ni frequently rates nj

with low ratings and (Ωs(i,j) − Ωsi) > 0 which impliesthat ni and nj share many interests.

Similar to social closeness, we also can replace Ωsiwith the average Ωs of a pair of transaction peers inthe system based on the empirical result. For example,in Overstock, the average, maximum and minimuminterest similarity between a pair transaction peers are0.423, 1, and 0.13.

4.3 Combined social closeness and similarity basedcollusion deterrenceCombining Formulas (6) and (8), we get:

r(i,j)(Ωc,Ωs) = r(i,j)·α·e−(

(Ωc(i,j)−Ωci

)2

2|maxΩci−minΩci

|2 +(Ωs(i,j)

−Ωsi)2

2|maxΩsi−minΩsi

|2 ),

(9)which simultaneously considers social closeness and in-terest similarity. For example, if two low-reputed nodesrating each other with high frequency have a veryclose social relationship (i.e., high Ωc(i,j) ) but share fewcommon interests (i.e. low Ωs(i,j) ), they are more likelyto collude with each other. This is because two nodeshave low probability to frequently request resourcesfrom each other if they share few common interests, andit is unlikely that a node will request a resource froma low-reputed node. Let us use Hc and Lc to denotevery high and low social closeness and use Hs andLs to denote very high and low interest similarity. AsFigure 6 shows, the rating values between the nodes thathave (Hc, Hs), (Hc, Ls), (Lc, Hs), and (Lc, Ls) are greatlyreduced. Therefore, based on Formula (9), the influencesof the collusion listed in B1-B4 are reduced.

SocialTrust can be executed in a centralized or dis-tributed manner. In the centralized system, a centralizedreputation manager manages all node reputation valuesand social information, and adjusts the values based onsocial closeness and similarity. As a distributed Social-Trust system is more challenging and suitable for large-scale distributed P2P networks, here, we introduce howSocialTrust is executed in a distributed manner. It can beeasily adapt to the centralized reputation system.

In a reputation system, one or a number of trust-worthy node(s) function as resource manager(s). Eachresource manager is responsible for collecting the ratingsand calculating the global reputation of certain nodes.Thus, each resource manager can keep track of the

rating frequencies and values of other nodes for thenodes it manages, which helps them to detect collu-sion in SocialTrust. A manager adjusts the ratings fromsuspected colluders when periodically calculating nodeglobal reputation. Suppose Mj is the resource managerof nj . Mj keeps the interest set and friendlist of nj . Aftereach reputation update interval T , Mj calculates thenumber of positive and negative ratings during T fromeach rater node ni for nj , denoted by t+(i,j) and t−(i,j) .

SocialTrust sets the thresholds for positive rating fre-quency and negative rating frequency of a node, denotedby T+

t and T−t from empirical experience. For example,

in Overstock, the average, maximum and minimumnumbers of positive ratings of a node per month are1.75, 21 and 1, while those of negative ratings are 1.84,2 and 1. When t+(i,j)>T+

t or t−(i,j)>T−t , which means that

ni is a suspected colluder, and Mj does not have interestset and friendlist of rater ni, it contacts ni’s reputationmanager Mi for the information. Based on the calculatedΩc(i,j) and Ωs(i,j) and nj ’s reputation, Mi makes furtherjudgement and adjusts the r(i,j) accordingly.

Specifically, SocialTrust sets a threshold for globalreputation (R) of a low-reputed node, denoted by TR. Italso sets high and low thresholds for Ωc(i,j) and Ωs(i,j)to represent the degree of social closeness and interestsimilarity between a pair of nodes, denoted by Tch , Tcl ,Tsh , and Tsl , respectively. If t+(j,i)>T+

t , which means nj

also frequently rates ni with positive ratings, if (1) theirsocial closeness is low (Ωc(i,j)<Tcl ) (B1), (2) their socialcloseness is high (Ωc(i,j)>Tch ) and nj is a low-reputednode (Rj<TR) (B2), or (3) their interest similarity islow (Ωs(i,j)<Tsl ) (B3), Mi adjusts r(i,j) according toEquation (9). If t−(i,j)>T−

t , which means ni frequentlyrates nj with negative ratings, and their interestsimilarity is high (Ωs(i,j)>Tsl ) (B4), Mi adjusts r(i,j).

4.4 Resilience to falsified static social informationSince SocialTrust depends on the social network to com-bat collusion, colluders may manipulate the social net-work to counterattack SocialTrust. Recall that SocialTrustidentifies reputation raters and ratees with very highor very low social closeness or interest similarity. Thus,colluders would fabricate static social network informa-tion (i.e., social relationships and interests in profiles)to keep their social closeness and interest similarity at amoderate level in order to avoid being detected. In orderto strengthen the ability of SocialTrust in combatingcollusion, we further improve the social closeness andinterest similarity measurement’s accuracy. We considerweight for the closeness of different social relationships.For example, kinship relationship should have higherweight than the friendship relationship. The social close-ness between two nodes in Formula (3) is updated to:

Ωc(i,j) =

⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩

(∑

l λ(l−1)wdl

)·f(i,j)∑|Si|

k=0f(i,k)

, ni and nj are adjacent,

∑k∈|Si∩Sj |

Ωc(i,k)+Ωc(k,j)

2non-adjacent nodes, k �= ∅,

min1≤i≤n

Ωc(ki,ki+1)non-adjacent nodes, k = ∅,

(10)where wdl

denotes the weight of the lth social relation-ship between ni and nj in the relationship list sorted


7

in descending order of the relationship weight, and λ ∈[0.5, 1] denotes a constant relationship scaling weight.

We also consider the weight of each node’s interest.We use ws(i,l) to denote the weight of node ni on interestl. It equals the percent of node ni’s requests on interestl in all of its requests. Then, the interest similarity inFormula (11) is updated to:

Ωs(i,j) =

∑l ws(i,l) · ws(j,l)

min(|Vi|, |Vj |) , l ∈ {Vi ∩ Vj}. (11)

Thus, SocialTrust depends not only on the static socialnetwork information but also the real node interaction inthe social network and resource requests, which preventsthe colluders from manipulating the social information.

To avoid being detected due to B1, colluders wouldtry to increase their social closeness by increasing theirtotal number of social relationships. As shown in Equa-tion (10), the social closeness Ωc(i,j) is determined notonly by the number of social relationships but alsothe interaction frequency. Although adding more socialrelationships can increase Ωc(i,j) , the increment can bevery small if the interaction frequency coefficient f(i,j)is low (i.e., the nodes actually are not socially close).To avoid being detected due to B2, colluders would tryto reduce the number of social relationships to gain amedian social closeness value. Similarly, a pair of nodesni and nj with high interaction frequency f(i,j) (i.e.,the two nodes are really socially-close) still have largesocial closeness value Ωc(i,j) . As SocalTrust considers theweight of social closeness and exponentially decreasesthe effect of the closeness weight on the final social close-ness metric, the calculated Ωc(i,j) can more accuratelyreflect the node social closeness. If two colluders add orreduce low-weight social relationships between them, itonly slightly changes the social closeness.

To avoid being detected due to B3, colluders wouldtry to increase their common-interests. The colludersmay fill out false interest information in their profilesto match their colluders’ interests to gain a reasonablyhigh interest similarity value in SocialTrust. As shown inEquation (11), in addition to a node’s interests, its per-cent of requests on the interests is also considered whencalculating the interest similarity Ωs(i,j). Although a col-luder’s profile lists many interests matching the boostingcolluder’s interests, their social interest similarity Ωs(i,j)

is still small if the colluder does not have many requestson the interests, which implies that it really has no suchinterests. To avoid being detected due to B4, a colludermay reduce many common interests in its profile withits ratee to gain a moderate interest similarity with theratee. However, the colluder’s frequent requests on thedeleted common interests still reveals that these are itsinterests. As shown in Equation (11), a colluder’s manyrequests on these interests still leads to high interestsimilarity value with its ratee.

In conclusion, it is difficult for colluders to manipulatesocial network’s information to counterattack Social-Trust. Since the underlying business model of collud-ers is to gain benefits from collusion, by preventingcolluders from gaining high reputations value throughsuspicious behaviors, the colluders’ underlying businessmodel will be destroyed. Then nodes should not haveincentives to collude each other by using SocialTrust.

5 PERFORMANCE EVALUATION

5.1 Experimental setup

Network model. We built an unstructured P2P networkwith 200 nodes. According to the Overstock trace, thenumber of total interests in the P2P network was set to20, and the number of interests for each node was ran-domly chosen from [1,10]. Nodes with the same interestsare connected with each other, and a node requests re-sources (resource and service are interchangeable termsin this section) from its interest neighbors. In addition,we randomly assign [1-2] relationships between nodesin the system, and the colluders are randomly assignedwith [3-5] relationships that have the same weight.

As observed in Section 3, in the experiments, thefrequency at which a node requests resources in itsinterests conforms to a power law distribution. Eachnode can handle 50 requests simultaneously per querycycle. When selecting a service for its request, a noderandomly chooses a neighbor with available capacitygreater than 0 and reputation higher than TR = 0.01.

Simulation execution. The simulation proceeds insimulation cycles. Each simulation cycle has 30 querycycles. In each query cycle, each peer issues a queryif it is active. The probability that a node is activeis randomly chosen from [0.5,1]. Among the colluders,the nodes receiving ratings from other nodes are calledboosted nodes, and the nodes rating others are calledboosting nodes. In each query cycle, a boosting node ratesa boosted node [3-7] times on an interest randomlyselected from the interests of the boosted node. Eachexperiment has 50 simulation cycles. Each experimentis run 5 times, and the average of the results is the finalresult. The 95% of the confidential interval is reported.

Node model. We consider three types of nodes:pretrusted nodes, malicious colluders, and normalnodes. The pretrusted and normal nodes always provideauthentic resources to the requesters with a probabilityof 1 and 0.8, respectively. We use B to denote theprobability that a malicious node offers an authentic file(i.e., good behavior). Since colluders usually offer lowQoS [7], [8], we tested the performance of reputationsystems when B=0.2 and 0.6, respectively. We randomlychose 9 pretrusted nodes and 30 colluders in the system.We assigned the social distance between colluders to1. Considering most transactions in Overstock occurbetween nodes with 1-3 social distance, we set the socialdistances between all other nodes to values randomlychosen from [1,3]. In these experiments, we focus on thecollusion behavior of B2 and B3. The social interactionfrequency f(i,j) equals the rating frequency (i.e., resourcetransaction frequency) of ni to nj . In a nutshell, colludershave relatively more social relationships, higher socialinteraction frequency, and less common interests.

Collusion model. We consider the following threemajor collusion models [7]: the pair-wise collusionmodel (PCM), the multiple node collusion model(MCM), and the multiple and mutual collusion model(MMM). We consider positive ratings among colludersin the experiments. Similar results can be obtained forthe collusion of negative ratings. In PCM, two colludersrate each other with a positive value at a high frequencyin order to raise each other’s reputation. In MCM, a


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number of boosting nodes rate a single boosted nodewith high frequency in order to boost the reputation ofthat node, but the boosted node does not rate boostingnodes back. In MMM, a number of boosting nodes rateboosted nodes with high frequency, and the boostednodes rate boosting nodes back.

Reputation model. The initial reputation of each nodein the network is 0. A client gives a service rating of1 when it receives an authentic service. Otherwise, therating is -1. Each node’s global reputation is updatedonce after each simulation cycle. The parameter α inthe Gaussian function was set to 1. We measured theperformance of the following three reputation systems:EigenTrust [10], eBay, and SocialTrust. We set the weightof reputations from pretrusted nodes in EigenTrust to0.5. We use a simulation cycle to represent a week ineBay. After each simulation cycle, we scale the reputa-tion of each node to [0,1] by Ri/

∑nk=0 Rk, where Ri is

accumulated ratings of ni.We assigned user IDs 1-9 to the pretrusted nodes

and IDs 10-39 to the colluders. With a collusion-resilientreputation system, we expect to see that the nodes withID 10-39 (i.e., colluders) have extremely low reputationvalues and the normal nodes have comparably higherreputation values. We also conducted experiments withdifferent numbers of nodes and colluders. The relativeperformance differences between the different systemsremain almost the same as those we will report. Thoughwe determine the experimental setting parameters ran-domly in their reasonable ranges, changing the param-eter values will not change the relative performancedifferences in a given experiment setup.

5.2 EigenTrust and eBay without colludersThe malicious nodes offer authentic files with prob-ability randomly selected from [0.2, 0.6]. Figure 7(a)shows the reputation distribution of EigenTrust withoutcolluders. It shows that the reputation values of themalicious nodes are very low and the reputation value ofpretrusted node and a small number of normal nodes arecomparatively high. This is because the node selectionstrategy in EigenTrust always retrieves files from thenodes with reputation larger than the threshold Tr. Sincethe pretrusted nodes and normal nodes offer more au-thentic files than malicious nodes, the reputation valuesof these nodes are much higher than malicious nodes.

Figure 7(b) plots the reputation distribution of nodesin eBay without colluders. It shows the reputation valuesof the nodes are distributed relatively evenly and nodeswith IDs in 10-39 have lower reputation. Since themalicious nodes have a high probability to send inau-thentic files to other nodes, the reputation values of themalicious nodes are lower than other nodes. Nodes with

lower reputation values attract less traffic requests. Sincein eBay, a node’s reputation increase is only determinedby whether the node offers more authentic files thaninauthentic files in each simulation cycle, the nodes withB>0.5 are possible to have good reputation values. Incontrast, in EigenTrust, every misbehavior of a node iscounted into the reputation calculation. This causes thereputation values of malicious nodes in EigenTrust to belower than those of malicious nodes in eBay.

Figure 7(c) compares the percent of the service queriessent to malicious nodes in EigenTrust and eBay. It showsthat the percent of the services provided by the maliciousnodes in EigenTrust is much less than eBay. In eBay,since the reputation of a node is only based on whetherit provides more authentic products than inauthenticproducts in a simulation cycle, the reputation updates ineBay are slow. Therefore, it takes a long time for a goodnode to gain a high reputation and for a malicious nodeto receive a low reputation. This allows a large amountof service queries to be sent to the malicious nodes.

5.3 Pair-wise collusion (PCM)We first show the effectiveness of EigenTrust, eBay andSocialTrust in thwarting pair-wise collusion with col-luders offering authentic services with 0.6 probability(B=0.6). The colluders rate each other with high fre-quency of 20 ratings per query cycle. Figure 8(a) showsthe reputation distribution of all nodes in EigenTrust.We can see that colluders have much higher reputationsthan all other nodes. Also, the reputations of pretrustednodes are slightly higher than normal nodes but aresignificantly lower than colluders. Since the colludersbehave well with probability 0.6, they gain certain repu-tations. The colluders further increase the reputations ofeach other, which helps them to attract many service re-quests to further increase their reputations. Although thenormal nodes and pretrusted nodes offer good serviceswith probabilities of 0.8 and 1 respectively, their repu-tations are dramatically lower than colluders. Therefore,EigenTrust has low effectiveness in combating collusion,and its generated reputations cannot truly reflect thenode trustworthiness when B=0.6. Figure 8(b) plots thereputation distribution of all nodes in eBay. It showsthat the reputations of the colluders are much higherthan all other nodes. The reason is that eBay enablesthe colluders with B=0.6 to increase reputations. Also,the mutual positive ratings between colluders furtherboost their own reputations. Therefore, eBay also haslow effectiveness in combating collusion.

Comparing Figure 8(a) and Figure 8(b), the reputa-tions of colluders in EigenTrust are higher than thosein eBay, and the reputations of pretrusted and normalnodes in EigenTrust are much lower than those in eBay.


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Fig. 8: Reputation distribution in PCM with B=0.6 (pretrusted nodes: 1-9, colluders: 10-39).

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This is because in EigenTrust, the ratings from nodes areweighted based on the reputations of the nodes. Sincethe ratings from colluders with high reputation havehigh impact on the reputation calculation, the reputationvalues of the colluders can be quickly boosted. In eBay,the contribution of the ratings from the colluders islimited since no matter how frequently a node ratesthe other node in a simulation cycle, eBay only countsall the ratings as one rating. Therefore, eBay constrainsreputation increase caused by collusion, leading to muchlower reputations of colluders.

Figures 8(c) and (d) show the reputation distributionsof the nodes in EigenTrust and eBay with SocialTrust,respectively. We can see that the colluders in both figureshave much lower reputation values than those in Fig-ures 8(a) and (b). The results show that SocialTrust canhelp EigenTrust and eBay to effectively thwart collusion.SocialTrust identifies suspected colluders based on socialcloseness and distance, and adjusts their reputation. Thiscauses the colluders in SocialTrust to finally receivesignificantly low reputations. Since no nodes chooselow-reputed nodes for services, SocialTrust effectivelycounters the collusion.

Next, we measure the reputation distribution of nodeswhen B=0.2 in different systems. Figure 9(a) shows thereputation distribution of nodes in EigenTrust. We seethat EigenTrust is able to reduce the reputation valuesof the colluders. Although colluders rate each otherfrequently, the weight of their ratings is very low dueto their low-QoS and reputations. Therefore, they finallyreceive low reputations and fewer service requests. Sincethe pretrusted nodes always behave well, they continu-ously receive high reputation values, finally gaining highreputations. We also notice that some normal nodes havehigh reputations while others have lower reputations. Atthe initial stage, a node randomly chooses from a num-ber of options with the same reputation value 0. Sincethe chosen node earns reputation, it subsequently hasa higher probability to be chosen. Therefore, EigenTrustcan counter collusion when the colluders offer low-QoS.

Figure 9(b) shows the reputation distribution of nodesin eBay. The reputations of colluders are much lower

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(b) EigenTrust with SocialTrust.Fig. 10: Reputation distribution in PCM with compromised pretrustednodes with B=0.2 (pretrusted nodes: 1-9, colluders: 10-39).

than those of the pretrusted nodes and normal nodes.The colluders receive low ratings from normal nodes dueto their high probability of misbehavior. Although thecolluders rate each other with high frequency to boosttheir reputations, eBay disregards the ratings from thesame rater in the same simulation cycle, leading to alow final reputation. Because colluders still receive highunweighted ratings with probability of 0.2, they earnslightly higher reputations than in EigenTrust.

Figures 9(c) and (d) show the reputation distributionof nodes in EigenTrust and eBay with SocialTrust. Bothfigures show that the reputation values of colludersare nearly 0. That is, SocialTrust can effectively combatcollusion nodes. By considering social closeness and theinterest relationship between nodes, SocialTrust reducesthe impacts of the ratings from the potential colludersand reduces the reputation values of the colluders.

5.4 Pair-wise collusion (PCM) with compromisedpretrusted nodesWe consider a scenario where B=0.2 and compromisedpretrusted nodes are involved in the collusion. We ran-domly selected 7 nodes from the pretrusted nodes andlet them randomly select a colluder with which to col-lude. We set the social distance between a compromisedpretrusted node and its conspired colluder to 1.

Figure 10(a) shows the reputation distribution of thenodes in EigenTrust. Comparing Figure 10(a) with Fig-ure 9(a), we find that compromised pretrusted nodesgreatly boost the reputations of themselves and collud-ers, and they reduce the reputations of normal nodes


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accordingly. This is due to three reasons. First, the rat-ings of pretrusted nodes have higher weight and theyrate highly on the colluders, causing the reputationsof the colluders in collusion with the pretrusted nodesto increase. Second, because of the high reputations ofthese colluders, their ratings for the pretrusted nodesalso have higher weight, further boosting pretrustednodes’ already high reputations. Third, as the colludersmutually rate each other with high frequency, the rep-utations of all colluders are boosted. The result impliesthat malicious nodes can take advantage of EigenTrust’spretrusted node strategy by compromising these nodes,which helps them to quickly boost their own reputations.EigenTrust cannot deal with the challenge of collusioninvolvement of compromised pretrusted nodes.

Figure 10(b) shows the reputation distribution ofthe nodes in EigenTrust with SocialTrust in the samescenario. We observe that high-reputed nodes areskewed among normal nodes and the non-compromisedpretrusted nodes. The reputations of the colluders andpretrusted nodes involved in collusion have nearly 0 rep-utations. The pretrusted nodes have high probability toprovide authentic services and receive high reputationsaccordingly. SocialTrust detects the pairs of suspiciouscolluders, including the compromised pretrusted nodes,which have a high mutual rating frequency. It then ad-justs their reputations according to their social closenessand interest similarity. Therefore, although a compro-mised pretrusted node initially has a high reputation, itsreputation eventually drops to a low value. The resultsdemonstrate the capability of SocialTrust in counteringcollusion even when pretrusted nodes are compromised.

5.5 Multiple node collusion (MCM)In the multiple node collusion model, among the 30colluders, 7 nodes are randomly selected as the boostednodes, and all other colluders randomly select one ofthe boosted nodes to collude with. Figure 11(a) showsthe reputation distribution of nodes in EigenTrust whenB=0.6. It demonstrates that some colluders, which areboosted nodes, have very high reputations while othercolluders, which are boosting nodes, have very low

reputations. This is caused by two reasons. First, asthe colluders offer authentic services to others withprobability of 0.6, they can initially gain reputations. Sec-ond, since the boosted nodes frequently receive positiveratings from several boosting nodes whose reputationvalues are not low, the reasonable rating weight ofthe boosting nodes can greatly increase the reputationvalue of the boosted nodes. The boosting nodes do notreceive frequent ratings from the boosted nodes. As theboosted nodes receive more and more service requests,the boosting nodes receive fewer and fewer requests,causing fewer opportunities to raise their reputations.

Figure 11(b) plots the reputation distribution of thenodes in eBay. It shows that the reputation values ofsome of the colluders are much higher than other nodesin the system while other colluders have comparativelylower reputations. This is due to the same reason in Fig-ure 11(a). Figure 11(c) plots the reputation distributionof nodes in EigenTrust with SocialTrust. By comparingit to Figure 11(a), we see that SocialTrust can effectivelyreduce the reputation values of both boosted and boost-ing nodes in EigenTrust. Although boosted nodes canreceive a large number of positive ratings from boostingnodes, the values of these ratings are reduced accordingto the social and interest relationship between the ratersand ratees. Meanwhile, due to the low reputation valuesof those boosting nodes, the weights of their ratings arevery low. Therefore, it is difficult for them to increase thereputation values of boosted nodes even with high ratingfrequency. Figure 11(d) shows the reputation distributionof the nodes in eBay with SocialTrust. It shows that So-cialTrust can effectively fight against collusion. Althoughthe boosting nodes can increase the reputation values ofthe boosted nodes as shown in Figure 11(b), SocialTrustreduces the impact of the rating between the colludersbased on their social closeness and interest similarity,and the reputation values of the colluders are reducedsignificantly in SocialTrust.

Next, we changed the probability that the colludersprovide authentic services to 0.2. Figure 12(a) shows thereputation distributions of the nodes in EigenTrust. Itshows that the reputations of the colluders including


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(d) eBay with SocialTrust.Fig. 14: Reputation distribution in MMM with B=0.2 (pretrusted nodes: 1-9, colluders: 10-39).

the boosted nodes are very low. Two factors contributeto this phenomenon. First, as the boosting nodes havelow reputation values, the weight of their ratings issmall. Therefore, their frequent ratings cannot affect thereputations of the boosted nodes. Second, as the boostednodes have a high probability to provide inauthenticservice, the ratings they receive from other normal nodesare very low. EigenTrust can counter MCM when the col-luders provide authentic services with low probability.

Figure 12(b) shows the reputation distribution ofnodes in eBay. We can see that the reputation values ofsome colluders are low while others are comparativelyhigh. Since the probability that the colluders offer au-thentic services is only 0.2, they receive low reputationvalues from normal nodes. The boosted nodes receivea large number of positive ratings from boosting nodes.Since the rating values from low reputed boosting nodesare not weighted, they can partially offset the negativeratings from normal nodes. Consequently, the reputationvalues of the boosted nodes are increased incrementally.Figures 12(c) and (d) show the reputation distributionof the nodes in EigenTrust and eBay with SocialTrust,respectively. The figures show that SocialTrust furtherreduces the reputation values of the boosted nodes.The results demonstrate the effectiveness of SocialTrustin combating collusion by considering their social andinterest relationships.

5.6 Multiple and mutual node collusion (MMM)

In this section, we evaluate the effectiveness of Eigen-Trust, eBay and SocialTrust in combating collusion inMMM. In every query cycle, each boosting node ratesrandomly chosen boosted nodes 20 times and theboosted node rates its boosting nodes 5 times. Fig-ures 13(a) and (b) show the reputation distribution of thenodes in EigenTrust and eBay, respectively. Comparingthese two figures to Figure 11(a) and (b), we see thatin MMM, the reputation values of both boosted nodesand boosting nodes are much higher. Since the colludersoffer authentic services with probability 0.6, they gain acertain amount of reputations. In MMM, as the boostednodes with high reputations rate the boosting nodes

back, the reputation values of boosting nodes are alsoenhanced. Then, the weights of boosting nodes’ ratingsincrease, which in return boosts the reputation values ofboosted nodes. Figures 13(c) and (d) illustrate the nodereputation distribution of nodes in EigenTrust and eBaywith SocialTrust, respectively. The figures show that thereputation values of colluders are greatly reduced dueto the same reasons as Figures 11(c) and (d).

Figure 14(a) shows the reputation distribution of thenodes in EigenTrust in MMM with B=0.2. ComparingFigure 14(a) with Figure 12(a) for MCM, we find that inFigure 14(a), some colluders have extremely high reputa-tion, which are even higher than those of the pretrustednodes. These colluders are the boosted nodes. In MMM,the boosted nodes and boosting nodes mutually rateeach other. Therefore, the reputation values of the boost-ing nodes are initially low, and they are increased asthe boosted nodes having high rating weights rate themback. The boosting nodes then in turn greatly increasethe reputation values of the boosted nodes. Althoughboosted nodes rate boosting nodes at a lower frequency,the boosted nodes still reach very reputations.

In PCM with B=0.2 (Figure 9(a)), the colluders cannotboost their reputations because their rating frequency of20 per query cycle is not high enough to offset the lowreputations from normal nodes. In contrast, in MMM,a boosted node receives 80 ratings per query cycle onaverage from boosting nodes. Therefore, although theyreceive low ratings from normal nodes, their reputationscan still be increased. The results demonstrate thatEigenTrust is not effective in countering MMM wherenodes rate each other with a high frequency.

Comparing Figure 14 and Figure 13, the reputa-tion values of colluders in EigenTrust with B=0.2 areeven higher than those with B=0.6. Those colluders areactually boosted nodes. With B=0.6, as the boostingnodes gain some reputations, they receive certain ser-vice requests from normal nodes. Therefore, the boostednode receives less service requests and lower reputa-tion values. When B=0.2, boosting nodes have verylow reputation and subsequently cannot receive manyservice requests from the normal nodes. Consequently,the boosted nodes receive more service requests and


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Fig. 15: Reputation distribution in MCM and MMM with compromised pretrusted node with B=0.2 (pretrusted nodes: 1-9, colluders: 10-39).

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comparatively higher reputations.Figure 14(b) shows the reputation distribution of the

nodes in eBay. Comparing this figure with Figure 12(b)for MCM, we find that the reputation values of boostingnodes in MMM are slightly higher. The reason is thatthe boosting nodes also receive ratings from boostednodes, which increases the reputation values of theboosting node. Figures 14(c) and (d) show the reputationdistribution of the nodes in EigenTrust and eBay withSocialTrust, respectively. Comparing these two figuresto Figures 14(a) and (b), we see that SocialTrust caneffectively reduce the reputation of the colluders due tothe same reasons as Figures 11(c) and (d).

5.7 Node collusion (MCM and MMM) with compro-mised pretrusted nodesFigure 15(a) and (b) demonstrate the reputation distri-bution of the nodes in EigenTrust in MCM and MMM,respectively, when compromised pretrusted nodesare involved in collusion with B=0.2. Colluders andpretrusted nodes collude in the same way as Figure 10.Comparing Figure 15(a) to Figure 12(a) for MCM, we seethat when pretrusted nodes are involved in collusion,the reputations of some colluders increase greatly whilethose of pretrusted nodes decrease. Because of B=0.2,boosting nodes have low reputations and a low weightfor their ratings. As shown in Figure 12(a), their frequentratings on the boosted nodes cannot greatly increasetheir reputations. The reputation values of the pretrustednodes are high. Therefore, when pretrusted nodes arecompromised, as shown in Figure 15(a), their ratingsgreatly increase the reputations of the boosted nodes,which attract many requests from the pretrusted nodes.

Figure 15(b) shows the reputation distribution of thenodes in EigenTrust in MMM. It shows that the reputa-tion values of colluders are 3-4 times higher than thoseof the colluders when pretrusted nodes are not involvedin collusion (Figure 14(a)). Because the pretrusted nodesgive positive ratings on colluders with high frequency,the reputation values of the boosted nodes becomevery large, and the weight of their ratings increases

accordingly. This greatly increases the reputations ofcolluders through frequent ratings between each other.Figures 15(c) and (d) show the reputation distributionof the nodes in EigenTrust with SocialTrust in MCMand MMM, respectively. The figures show that boththe colluders and compromised pretrusted nodes havelow reputations. It means that SocialTrust can still effec-tively reduce the reputation values of the colluders andcompromised pretrusted nodes based on the social andinterest relationship between the nodes.5.8 Resilience to falsified social informationIn this experiment, we assume colluders falsify theirsocial information so that each pair of colluders hasonly one social relationship and identical interests. Thenumber of identical interests is randomly chosen from[1-10]. The frequency of requests on a node’s interestwc(i,l) equals the percent of requests on this interest inall the node’s requests in a simulation cycle. Previousexperimental results show that when B=0.6, both Eigen-Trust and eBay with SocialTrust can effectively detect thecolluders. We now test their resilience to falsified socialrelationships and interests.

Figure 16, Figure 17, and Figure 18 show the rep-utation distribution of both EigenTrust and eBay withSocialTrust with falsified social information in PCM,MCM and MMM. By comparing the results in these fig-ures with the results with accurate social information inFigure 8(c) and (d), Figure 11(c) and (d), and Figure 13(c)and (d) for each model, we find that although the repu-tation values of colluders are higher with falsified socialinformation than those with accurate social information,their reputation values are still significantly lower thanthose of other nodes. It implies that SocialTrust stillcan effectively thwart collusion even when the socialinformation is falsified. In addition to the social infor-mation in user profiles, SocialTrust also considers userinteraction frequency and resource request frequencywhich cannot be falsified and can truly reflect real usersocial closeness and social interest.5.9 Efficiency and effectiveness in combating col-luders


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We measured the number of simulation cycles untilthe reputation values of colluders are lower than 0.001.Figure 19(a) shows the 1st percentile, 99th percentileand median values of the number of simulation cyclesin EigenTrust, SocialTrust and eBay when B=0.2. Wesee that both EigenTrust and SocialTrust take about 6-8 simulation cycles, while eBay takes about 25 simu-lation cycles. This is because the reputation calculationmethods in EigenTrust and SocialTrust make the nodereputation values converge very fast, while the repu-tation calculation in eBay is not sufficiently efficient.Figure 19(b) shows the number of simulation cyclesin EigenTrust and SocialTrust when B=0.6. It exhibitssimilar performance as in Figure 19(a) due to their fastreputation convergence. We did not plot eBay in thisfigure because eBay cannot detect colluders when B=0.6.We also measured the number of simulation cycles ofEigentrust, SocialTrust and eBay in PCM and MCM. Theexperimental results are almost the same as those inMMM, which confirms the high efficiency of collusiondeterrence in EigenTrust and SocialTrust. Due to thespace limit, we do not show these experimental here.

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the average reputation ofcolluders increases slightlywhen the social distanceincreases from 1 to 2,but decreases slightly whenit increases to 3 subse-quently. Recall that the dis-tance between a pair ofnodes was randomly cho-sen from [1,3]. Social dis-tance 1 means social closeness is high while socialdistance 3 means social closeness is low. As shown inEquation (9), the rating between two nodes with tooclose or too far-away social relationship is reduced more.We also see that the reputation values of the colludersare constantly lower than those of normal nodes even

when the colluders have moderate social distance 2.This result confirms the effectiveness of SocialTrust inmitigating the adverse influence of collusion even whencolluders keep their social distance at a normal level.SocialTrust reduces the rating value based on both socialcloseness and interest similarity, which are very difficultto be manipulated as explained in Section 4.4.

5.10 Percentage of requests sent to colludersTable 1 shows the percentage of requests sent to col-luders in each system in different collusion models withB=0.2 and B=0.6, respectively. In the table, “(Pre)” meansthat the pretrusted nodes are involved in collusion.First, we see that in all three collusion models, col-luders receive more requests when B=0.6 than whenB=0.2 in most systems. This is because colluders withhigher probability to provide authentic services havehigher reputation values initially, which leads to higherweight for their ratings and hence further enhance theirreputations, finally attracting more requests from thenormal nodes. Second, comparing the results in differentcollusion models, we find that more service requests aresent to colluders in MMM and PCM than MCM. Thisis because colluders in MMM and PCM mutually rateeach other with high frequency while boosting nodes inMCM do not receive ratings from boosted nodes.

TABLE 1: Percentage of the requests sent to colluders.

Pair-wise collusion model (PCM)B=0.2 B=0.6

eBay 6% eBay 17%EigenTrust 17%EigenTrust 24%EigenTrust (Pre) 22%EigenTrust (Pre) 24%eBay+SocialTrust 3% eBay-Social 2%EigenTrust+SocialTrust 2% EigenTrust+SocialTrust 3%EigenTrust+SocialTrust (Pre)2% EigenTrust+SocialTrust (Pre)2%

Multiple node collusion model (MCM)B=0.2 B=0.6

eBay 7% eBay 16%EigenTrust 7% EigenTrust 15%EigenTrust (Pre) 9% EigenTrust (Pre) 10%eBay+SocialTrust 3% eBay+SocialTrust 2%EigenTrust+SocialTrust 2% EigenTrust+SocialTrust 2%EigenTrust+SocialTrust (Pre)2% EigenTrust+SocialTrust (Pre)2%

Multiple and mutual node collusion model (MMM)B=0.2 B=0.6

eBay 8% eBay 17%EigenTrust 19%EigenTrust 21%EigenTrust (Pre) 21%EigenTrust (Pre) 24%eBay+SocialTrust 2% eBay+SocialTrust 2%EigenTrust+SocialTrust 3% EigenTrust+SocialTrust 3%EigenTrust+SocialTrust (Pre)4% EigenTrust+SocialTrust (Pre)3%

Third, in EigenTrust and eBay in all collusion models,the percent of requests sent to colluders when pretrustednodes are involved in collusion is higher than whenthey are not involved in collusion in most cases. Thisis because the pretrusted nodes increase the reputationvalues of colluders, which subsequently attract more ser-vice requests. Finally, we see that SocialTrust reduces thepercent of requests sent to colluders to 2%−4% in differ-ent systems and collusion models, even when pretrustednodes are involved in the collusion. By considering thesocial closeness and interest similarity, SocialTrust ad-justs the ratings between the suspected colluders. Then,these nodes receive low reputations and fewer servicerequests, which discourages the collusion behaviors.

6 CONCLUSIONDespite the effectiveness of reputation systems in findingdeceptive peers according to the reputation values, they


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are vulnerable to collusion. Although many reputationsystems try to reduce the influence of collusion on repu-tation values, they are not sufficiently effective in coun-tering collusion. After examining the Overstock transac-tion trace of reputation ratings, we identified suspiciouscollusion behavior patterns. According to the behaviorpatterns, we propose the SocicalTrust mechanism thatleverages a social network to combat collusion. Exper-iment results show SocicalTrust greatly enhances thecapability of eBay’s reputation system and EigenTrustin countering collusion. SocicalTrust can even detect col-luders with compromised pretrusted high-reputed nodesand falsified social information. In our future work, wewill further investigate other collusion patterns, securityissues and attack models in the SocialTrust design.

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Ze Li Ze Li received the BS degree in Electron-ics and Information Engineering from HuazhongUniversity of Science and Technology, China, in2007. He is currently a Ph.D. student in the De-partment of Electrical and Computer Engineer-ing of Clemson University. His research interestsinclude distributed networks, with an emphasison peer-to-peer and content delivery networks.He is a student member of IEEE.

Haiying Shen Haiying Shen received the BSdegree in Computer Science and Engineeringfrom Tongji University, China in 2000, and theMS and Ph.D. degrees in Computer Engineeringfrom Wayne State University in 2004 and 2006,respectively. She is currently an Assistant Pro-fessor in the Department of Electrical and Com-puter Engineering at Clemson University. Herresearch interests include distributed computersystems and computer networks, with an em-phasis on P2P and content delivery networks,

mobile computing, wireless sensor networks, and grid and cloud com-puting. She was the Program Co-Chair for a number of internationalconferences and member of the Program Committees of many leadingconferences. She is a Microsoft Faculty Fellow of 2010 and a memberof the IEEE and ACM.


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