Credible Recommendation Exchange Mechanism for P2P Reputation Systems

Transcription

1 Credible Recommendation Echange Mechanism for P2P Reputation Systems Eleni Koutrouli Aphrodite Tsalgatidou Department of Informatics & Telecommunications, National & Kapodistrian Uniersity of Athens, T.Y.P.A. Buildings, Panepistimiopolis, Ilisia, Athens,Greece Tel.: {ekou, ABSTRACT The alue of Reputation Systems has been widely recognized for P2P other distributed applications. This alue depends on how credible reputation systems can be, as they face a lot of attacks arious types of misbehaior. Recommendation free riding badmouthing are two of the most important problems in reputation systems. Incenties constitute an important tool for alleiating such problems. We propose a credit-based recommendation echange mechanism which proides incenties for honest participation in the reputation system. Our mechanism uses payments for recommendations, which depend on the trustworthiness of peers regarding the recommendations they gie. No currency is circulated in the network. Peers hae accounts, which are credited or debited according to recommendation transfers, while payment alues are defined according to the recommendation trustworthiness of peers. We analyze the proposed payment scheme proide simulation results to alidate our analysis. We also discuss ways of secure recommendation echange related challenges. Categories Subject Descriptors C.2.1 [Computer-Communication Networks]: Network Architecture Design - Distributed networks; C.2.4 [Computer-Communication Networks]: Distributed Systems - Distributed applications; H.4.2 [Information Systems Applications]: Types of Systems Decision support; H.4.3 [Information Systems Applications]: Communications Applications; K.4.4 [Computers Society]: Electronic Commerce Security; General Terms Algorithms, Design, Security. Keywords Reputation systems, payment-based recommendations, credibility, p2p, peer-to-peer 1. INTRODUCTION Reputation systems constitute an important trust management tool in online communities (P2P communities, e-commerce, ad-hoc applications etc.) aiming to support trust decisions, i.e. to help an Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee proided that copies are not made or distributed for profit or commercial adantage that copies bear this notice the full citation on the first page. To copy otherwise, or republish, to post on serers or to redistribute to lists, requires prior specific permission /or a fee. SAC 13, March 18-22, 2013, Coimbra, Portugal. Copyright 2013 ACM /13/03 $ entity to decide whether to trust another entity hae a transaction with it or not, by using past behaior as a predictor of future behaior. Reputation systems for P2P communities (e.g. [10], [12]), where the control administration is distributed among the community members, are susceptible to unfair or deceitful raters to arious types of attacks ([7], [9]) such as dishonest ratings (badmouthing attack), using different identities in order to deceit the system (Sybil attacks) lack of participation in the rating process (recommendation free riding). We beliee that incenties are an important tool for honest participation in a reputation system; therefore, we hae designed an incentie-based recommendation process for a reputation system for P2P communities which we present in this paper. This process relates the honest recommendation behaior with the ability to acquire recommendations, thus supporting more credible trust decisions. The recommendation echange mechanism uses payments based on the recommendation reputation of the participators peer accounts which are updated according to recommendation echanges. Ιn the following, we describe the aforementioned approach, by presenting the basic concepts of the proposed reputation system in section 2, followed by a description of the proposed recommendation echange mechanism payment scheme in section 3 along with a discussion on account management; then, in section 4, we proide eamples as well as simulation analysis results; in section 5 we present related work whereas in section 6 we outline our conclusions. 2. REPUTATION SYSTEM CONCEPTS AND COMPONENTS In the following we outline the basic concepts of a reputation system that integrates the proposed recommendation mechanism. In the descriptions we are using the eample of a P2P community, whereas a detailed description of the concepts, algorithms formulae can be found in [6]. P2P community: a community of indiiduals (peers) which offer serices to each other. Each peer has an epertise in one or more fields. The leel of epertise is epressed in a particular scale. A member of the community can claim the leel of epertise she has in each field, by, for eample, sending adertisements about oneself in a Distributed Hash Table (DHT) (e.g. Chord oerlay [13]). Peers can query the community to find eperts in a particular field by 1) searching the aailable adertisements in the oerlay for locating cidate eperts, 2) estimating a reputation measure for a particular epert which will indicate the epert s capability to proide the required serice of an acceptable quality. Based on these reputation alues peers can decide who they will transact with.

2 Transaction: serice echange between two peers. Various kinds of serices may be included, such as storing data, answering queries, e-commerce trade, etc. Transaction ealuation: after a transaction the peer (trustor) which receied a serice from another peer (trustee) rates the quality of the particular serice. Reputation: an indicator of the quality of the transactional behaior of a peer based on the ealuation of the preious transactions of this peer with others. Direct reputation: a reputation alue produced by the transaction ealuation alues concerning the direct transactions between the trustor the trustee. Indirect reputation: a reputation alue produced by the accumulation of third party recommendations. Oerall reputation: the reputation alue that is produced by the accumulation of direct indirect reputation. Recommendation: ealuation information regarding the transactional behaior of a peer which is gien to the trustor by a third party (recommender). It is based on the ealuation of local interactions between the recommender the trustee is accompanied by the confidence of the recommender about the ealuated reputation alue. Recommendation alue will be thus estimated on request as the direct reputation regarding the trustee. Confidence: the confidence measure for a direct reputation alue / recommendation Recommendation reputation: a measure of one peer s trustworthiness regarding the recommendations it gies. Each peer which receies recommendations for a particular peer (trustee) then transacts with the trustee, estimates the recommendation reputation of the recommenders by comparing the receied recommendations with the result of its subsequent transaction with the trustee. We use the following formula for computing recommendation reputation: Where: RecRep A,i 1 ( B ) Rec Re p A,i B * 1 RecValueB,A,C TransEalA,C, if RecValueB,A,C TransEalA,C Rec Re pthres { Rec Re p A,i B * 1 RecValueB,A,C TransEalA,C, otherwise RecRep A,i (B) is the recommendation reputation alue that peer A estimates for peer B after the i th transaction between them. RecValue,B,A C is the recommendation alue that peer B gies peer A about peer C. TransEal A,C is the ealuation of the transaction between A C from A s point of iew. RecRepThres is a threshold that is compared with the distance between the recommendation receied the actual ealuation of the transaction defines if the recommendation reputation of the recommender will be decreased or increased, according to (1). In our approach, transaction ratings, reputation alues, recommendations, confidence recommendation reputation take alues in the range [0,1]. (1) 3. RECOMMENDATION MECHANISM In order for the reputation system described earlier to achiee honest recommendations we designed a mechanism for recommendation echanges based on recommendation reputationdepended payments. This mechanism enhances the credibility of the reputation model by proiding incenties for honest recommendations reinforcing its robustness against bad mouthing recommendation free riding attacks. In this section we describe the proposed Credible Recommendation Echange Payment Scheme (CREPS). 3.1 Credible Recommendation Echange Payment Scheme The proposed recommendation echange mechanism (CREPS) is an accounting scheme where payments are used for recommendation echanges, based on the recommendation reputation of peers. Peers which participate in CREPS hae irtual accounts use them to make payments for acquiring recommendations. A peer A (recommendation ) which wants a recommendation from a peer B (recommendation ) pays a alue for it to B. The alue depends on both the recommendation reputation alues that A B hae estimated for each other (RecRep B (A) RecRep A (B)), according to (1). As eplained earlier, recommendation reputation (RecRep) alues range in [0,1]. Howeer, peer B will be asked for a recommendation from peer A only if RecRep A (B) >= t, also peer A will be able to acquire a recommendation from B only if RecRep B (A) >= t, where t, t (0,1) are thresholds for the recommendation reputation alues of the recommendation the respectiely. These thresholds are defined by the reputation system designer according to the credibility requirements of the application. The payment alue is a irtual amount which will be transferred between peers irtual accounts, without currency transfer. This is estimated according to the following formula: Rec Re p Rec Re p In our eample, this formula becomes Rec Re p A( B ). Rec Re p ( A ) B ( ) ( ) (2) We can easily notice that if RecRep () is lower than RecRep () then <1, while =1 or >1 in case that RecRep () is respectiely equal to or greater than RecRep (). When a peer joins the reputation system, it is gien an Initial Account Balance (IAB), which is a irtual amount defined in relation to the maimum number of recommendation echange transactions that we could permit a peer with low recommendation reputation alue to make, as it will be shown in the net section. The Account Balance (AB) of a peer is updated after each recommendation echange in which the peer participates either as a or as a. A peer will be able to ask for buy a recommendation, only if it has a positie AB. So, after a number or recommendation

3 sells a number y of recommendation buys that an entity does, if i is the i th payment the entity receies for a recommendation j is the j th payment it gies for a recommendation it asks for, then the account balance of the entity will be defined as follows: AB IAB y i j 0 i1 j1 In our proposal peer accounts are managed by special entities S i s, organized in a P2P oerlay, such as a DHT (e.g. 14]). Each S i is responsible for managing the AB of a number of peers. Encryption techniques are integrated in the message transferring protocol to ensure integrity non repudiation, similarly to [13]. The description of the protocol is out of the scope of this paper. CREPS relates honest recommendation proision with acquiring the maimum utility from the reputation system. Peers are motiated to proide honest recommendations so as to acquire high recommendation reputation (RecRep) thus be paid epensiely for their recommendations. In this way their AB is getting higher. Furthermore, peers which hae a high RecRep alue will pay less for recommendations buys (as aforementioned, if a recommendation has a higher RecRep alue than a recommendation, the will pay a alue <1). So, the higher the AB a recommendation requester has the more recommendations it can acquire. CREPS proides, thus, incenties for honest recommendation proision, in order for peers to achiee higher payments as recommendation s lower payments as recommendation s. In the design of the proposed recommendation payment echange mechanism the following attacks hae been considered: badmouthing recommendation free riding, as will be shown in the analysis of the net section. 4. EXAMPLES AND SIMULATION In the following, we eamine how the amount that a peer can earn or lose (in relation with the recommendation reputation of the entities inoled in the process) is deeloped while using the recommendation echange mechanism of CREPS. In order to do so, we first define the range of payment amount alues then we use 5 cases, with different relations between the recommendation reputation of the recommendation that of the recommendation. For each case we estimate the amount that a participator in CREPS can earn or lose, assuming it has made the same number of recommendation buys sells. This assumption is made to ensure that the participator s balance will be both reduced due to recommendation buys increased due to the same number of recommendation sells the final earn or loss will be clearly due to the relation of recommendation reputation alues. Finally, we show how a constantly dishonest recommendation behaior makes it impossible for a peer to participate in the recommendation echange mechanism. 4.1 Analysis of Payments Account Balances As already mentioned, in order for a peer to participate in the reputation system by proiding or acquiring recommendations it has to hae a minimum leel of credibility as a recommender. We thus set two thresholds (t t ) for the reputation recommendation alue that a peer should hae in order to buy or sell recommendations respectiely. Due to these thresholds, the minimum maimum payments for each recommendation echange, which are defined by (2), will be: Where: Min(Rec Re p( )) min Ma(Rec Re p ( )) ma Ma(Rec Re p Min(Rec Re p ( )) ( )) Min(RecRep (), Min(ReCRep ()) are the minimum alues that can be assigned to the recommendation reputation of a recommendation a recommendation, that is t t, respectiely, Ma(RecRep ()), Ma(RecRep () are the maimum alues that can be assigned to the recommendation reputation of a recommendation a recommendation, that is 1, since RecRep takes alues in [0,1]. So, min t ma 1 t, or 1 t, t For simplifying our formulas, we set the following parameters: Then: a 1 t 1a, 1b b 1 t 1 In order to ehibit how the AB of a peer eoles, we make the following assumptions: 1. A peer A buys recommendations from other peers also sells recommendations to other peers which ask for recommendations. 2. The number of recommendation buys (RecBuyNbr) is the same as the number of recommendation sells (RecSellNbr), i.e. RecBuyNbr = RecSellNbr = The amount u that A earns (/loses) is defined as follows: u i1 sell i j1 buy j Where: selli is the amount that A is paid in the i th recommendation sell, buyj is the amount A gets for its j th recommendation buy. 1 st case: In this scenario A always has a RecRep alue which is lower than the RecRep alue of the other peers with whom A echanges recommendations (A s counterparts). Then for the alues sell buy of a recommendation sell recommendation buy respectiely, the following should hold: sell t 1, 1 buy 1, t The amount u that A will earn (or lose) will then be: u i1 sell i j1 buy j ( a b),0 (3)

4 2 nd case: A always has a RecRep alue which is higher than the RecRep alue of the other peers with whom A echanges recommendations. Then: u ( sell i1 1 1, t selli j1 buy j buy t,1 ) 0, ( a b ) 3 rd case: Among recommendation buys recommendation sells, in y recommendation buys A has worse RecRep alue than the other peers from whom A buys recommendations in w recommendation sells A has worse RecRep alue than the RecRep alue of the peers to whom A sells recommendations. For the rest of recommendation echanges we assume that A s RecRep alue is the same with that of its counterparts. u selli sellk buy buy w a y b, 0 j l i1 k 1 j 1 l 1 4 th case: In y recommendation buys A has better RecRep alue than the other peers from whom A buys recommendations in w recommendation sells A has better RecRep alue than the RecRep alue of the other peers to whom A sells recommendations. For the rest of recommendation echanges we assume that A s RecRep alue is the same with that of its counterparts. y u selli sellk buy buy,w b y a j l 0 i1 k1 j1 l 1 5 th case: In y (y ) recommendation buys A has better (worse) RecRep alue than the other peers from whom A buys recommendations in w (w ) recommendation sells A has better (worse) RecRep alue than the RecRep alue of the other peers to whom A sells recommendations. For the rest of recommendation echanges we assume that the RecRep alue of A is the same with that of its counterparts. ' y y' u sell sell sell buy buy m i k l j i1 k 1 l ' 1 j1 m 1 n y' 1 From the aboe formula we infer that: u ' a y' b, b ya Our scenarios ehibit that after the same number () of buys sells the amount that a peer can earn (or lose) is bounded as shown in (3). By suitably setting the IAB of each peer we can ensure that a peer which does not participate honestly in the reputation system will stop being able to receie recommendations after a number of recommendation echanges. More specifically, for a peer which makes recommendation buys sells always has worse recommendation reputation alue than its counterparts, we can ensure that after a specific number () of recommendation buys the same number of recommendation sells the peer cannot participate in the recommendation echange mechanism any more. Number depends on the IAB the recommendation reputation thresholds according to (3) (4) is defined in (5): (4) buy n IAB a b 4.2 Simulation of a Peer s Account Balance We hae simulated the fluctuation (increase / decrease) of the account balance of a peer A after the same number of recommendation buys recommendation sells that it does, in arious cases. In our simulation eample that is illustrated in Figure 1 we hae set t = t = 0,5. Figure 1 shows A s account balance (AB) as estimated by increasing or decreasing the Initial Account Balance (IAB) by the profit or damage caused during recommendation echanges, in four scenarios, taking into consideration that AB should always be greater than or equal to 0. In each one of the used scenarios, which are described in the following, the recommendation reputation of peer A has a specific relationship with the recommendation reputation of its counterparts, which is the same in all recommendation echanges: Scenario 1. Peer A has greater recommendation reputation alue than this of its counterparts. Scenario 2: Peer A has the maimum recommendation reputation alue (RecRep Counterpart (A)=1) whereas its counterparts hae the minimum recommendation reputation alue (RecRep A (Counterpart) = 0,5). Scenario 3: Peer A has lower recommendation reputation alue than this of its counterparts. Scenario 4: Peer A has the minimum recommendation reputation alue (RecRep Counterpart (A)=0,5)) whereas its counterparts hae the maimum recommendation reputation alue (RecRep A (Counterpart)=1). In all scenarios, the increase or decrease of the AB of A is dependent on the relation between its own recommendation reputation alue the recommendation reputation of its counterparts. Figure 1 isualizes how the aforementioned relationship affects the maimum number of recommendation echanges that a peer can do when its recommendation reputation is lower than this of its counterparts (this maimum number equals 3 in scenario 4 10 in scenario 2). Our analysis shows that CREPS discourages badmouthing attack, as this kind of behaior will eentually make it impossible for the attacker to participate in the recommendation echange mechanism. In order to inestigate how the IAB should be defined in relation with the number of recommendation echanges that a peer could do while being more dishonest as a recommender than its counterparts, we hae eamined different cases which refer to different thresholds for recommendation, i.e. different policies regarding how strict the system is for allowing participation to recommendation s s. These policies are presented in Table 1. Figure 2 presents how IAB is affected for each policy by the maimum allowed number of recommendation buys sells of a non reputable recommender. Table 1. Strictness policies for participation in the reputation system as define by thresholds for buying selling Policy Threshold alues equal (medium) strictness for both t = t = 0,5 stricter for than for t = 0,4 t = 0,6 stricter for than for t = 0,6 t = 0,4 strict for, moderately strict strict for t = 0,8 t = 0,4 strict for, moderately strict for t = 0,4 t = 0,8 (5)

5 4.3 Discussion on Initial Account Balance When a peer s AB falls below IAB the peer will be tempted to enter the reputation system with a new identity, a behaior known as whitewashing attack. Creation of new identities by the same entity for the purposes of a dishonest recommender which wants to increase its AB in order to keep using the recommendation system should be discouraged. This can be done both in the application leel (an entity which enters a P2P community with a new identity will hae to start with a minimum reputation alue) also by using a suitable identity policy, which will control the identity creation procedure (e.g. [2]). Furthermore, dishonest recommenders, whose recommendation reputation alue as estimated by other peers falls below thresholds (t t ) are then preented from participating in CREPS, should be gien a chance to recoer their recommendation reputation, probably after a probationary period (as proposed in [5]). 20 Account Balance = IAB + Profit / Damage 14 IAB for different sticktness policies for participation in the reputation system AB 10 5 IAB Number of Recom. Echange Buys / Sells lower recom. reputation than its counterparts with the maimum difference lower recom. reputation than its counterparts higher recom. reputation than its counterparts with the maimum difference higner recom. reputation than its counterparts Maimum Number of permitted buys sells when recommendation reputation of A is always lower than its counterparts equal strictness for both (medium) stricter for than for strict for, moderately strict for stricter for than for strict for, moderately strict strict for Figure 1. Account balance fluctuation of a peer according to the relation between its recommendation reputation the recommendation reputation of its counterparts 5. RELATED WORK The work presented in the preious sections is based on the work that we hae proposed in [6]. More specifically, in [6] we presented a reputation system for P2P e-communities, with a detailed description of arious reputation components. In the current work we enhance the reputation system presented in [6] with a recommendation echange mechanism (CREPS) which uses recommendation reputation-based payments. Howeer, CREPS can be applied in any reputation system which uses recommendations monitors recommenders credibility. In the following we reiew the related work found in the literature along the following two aes: a) incenties for honest recommendation proision credit-based approaches b) micropayments token-based reputation mechanisms. Reputation systems should proide incenties to entities to participate actiely honestly in the reputation system, i.e. to proide honest recommendations constantly. Works which propose incenties for honest recommendation proision include probability-based ([8]) or credit-based approaches ([4], [5]). In [8] an agent which has been asked for recommendation from another agent sends back the recommendation with different probabilities according to the state of the requester agent (actie truthteller, actie liar, inactie truthteller, inactie liar newcomer). The less actie an entity is in proiding recommendations, the less possible that it receies helpful recommendations from others. Jurca Faltings [4] propose feedback payments, which are determined according to the honesty of the recommender. An entity can buy a recommendation about another entity from a special broker. After interacting with the trustee, the trustor can Figure 2. IAB in relation with the maimum number of recommendation echanges for different thresholds sell its feedback to the broker, but gets paid only if its report coincides with the net entity s report about the trustee. Payments are designed in such a way that dishonest agents should gradually lose their money, whereas agents which report truthfully at all times should not hae any money loss. A credit-based recommendation scheme is also proposed in [5], where credit discounts are gien to honest peers. In the credit based mechanism for recommendation echange of our proposal, payments for recommendations are determined according to the recommendation reputation alues of the requester the recommender, ensuring that the most reliable an entity is regarding proiding recommendations, the less it will pay for recommendation acquiring. Regarding the second ais of related work, a number of micropayment systems for P2P systems hae been proposed aiming mostly at dealing with the freeloading problem. These systems implement accounting mechanisms which are based either on creating transferring irtual coins ([3], [1], [18], [17]) or on using irtual accounts per user or node ([15], [11]). Hausheer et al. [3] use tokens for serice echange in P2P applications in order to cope with the free-riding problem. Users can clearly be identified through a permanent ID issued by a certification authority. Each peer holds an account with a specific number of tokens which hae been specifically issued to it. Peers echange their tokens for proiding receiing serices. They can also echange the collected foreign tokens against new ones issued them. Threshold cryptography is used for preenting token tampering. Similarly, in the P2P-Netpay micro-payment model [1] peers buy e-coins from a broker use them to pay for

6 downloading content from a peer endor. Peer endors redeem the collected e-coins for coins of their own. CPay [18] is a debitbased micropayment protocol. The broker is responsible for the distribution redemption of the coins the management of eligible peers called Broker Assistant (BA). Each BA is responsible for a number of peers, specifically for checking their coins authorizing their transactions. PPay [17] reduces broker workload by using transferable, self-managed coins, with an associated cost in scalability fraud detection. A different approach for payment mechanisms which is based on irtual accounts instead of tokens is followed in [15] [11]. KARMA, [15] is a general economic framework aiming at aoiding freeloaders in P2P systems, by keeping track of the resource consumption contribution of peers. Each participant is mapped to a single scalar alue, called Karma, which can be thought of as an account of irtual currency. A set of nodes keeps track of each node s Karma, updates it, by increasing or decreasing it when the node contributes or consumes resources. ARM [11] is an account-based reputation system. Information about the behaior of each node is monitored mapped to a reputation alue an account alue of the node. All serices are then priced based on node reputation. The higher the reputation alue of a node is the lower price the node will need to pay for a serice. Selfish nodes are thus preented from keeping their reputation alue at a low leel just aboe reputation threshold. Similarly to the last two of the described works (KARMA ARM), in the model that we propose there is no currency circulated in the system. Instead, peer accounts are updated according to the payments inoled in recommendation acquiring offering. The goal is to alleiate the free riding problem in reputation systems, i.e. the case where peers use third-party recommendations while at the same time do not proide honest recommendations to others. 6. SUMMARY AND CONCLUSIONS The main contribution of this work consists of a noel payment mechanism for fair recommendations that uses payments for recommendations based on the trustworthiness of peers regarding the recommendations they gie. No currency is circulated in the network. Peers hae accounts, which are credited or debited according to recommendation transfers, while payment alues are defined according to the recommendation credibility of peers. We hae analyzed the payments estimation the utility gained in arious scenarios hae performed a number of simulation eperiments, in order to show how incenties work; we mainly demonstrated how dishonest recommendation behaior preents recommendation acquiring, whereas honest recommendation behaior results in the maimum utility of the reputation system. Our future work includes encryption-based implementation of CREPS with the use of a decentralized PKI integration of CREPS in reputation systems for P2P communities. 7. REFERENCES [1] Chaudhary, K. & Dai, X., P2P-NetPay: An Off-line Micropayment System for Content Sharing in P2P-Networks, Journal of Emerging Technologies in Web Intelligence, 1, 1 (August 2009), [2] Friedman, E., Resnick, P.: The social cost of cheap pseudonyms. Journal of Economics Management Strategy, 10, 2 (2001), [3] Hausheer, D., Liebau, N. 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