An Adaptive Group-Based Reputation System in Peer-to-Peer Networks Liang Sun1 , Li Jiao1 , Yufeng Wang2 , Shiduan Cheng1 , and Wendong Wang1 1
State Key Laboratory of Networking and Switching, Beijing University of Posts and Telecommunications, Beijing, China {xiaodan, jiaoli, chsd, wdwang}@bupt.edu.cn 2 Department of Communication Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China
[email protected]
Abstract. As more and more P2P applications being popular in Internet, one of important problem to be solved is inspiring users to cooperate each other actively and honestly, the reputation mechanism which is a hot spot for P2P research has been proposed to conquer it. Because of the characters of virtuality and anonymous in the network, it is very easy for users with bad reputations to reenter the system with new identities to regain new reputations in the reputation systems. In order to get rid of the impact of whitewashers and improve the system performance and efficiency, we propose a new probability-based adaptive initial reputation mechanism. In this new mechanism, newcomers will be trusted based on system’s trust-probability which can be adjusted according to the actions of the newcomers. To avoid the system fluctuating for actions of a few whitewashers, we realize the new reputation mechanism in system with group-based architecture, which can localize the impact of whitewashers in their own groups. Both performance analysis and simulation show that this new adaptive reputation mechanism is more effective.
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Introduction
Oram [16] gives a simple definition of peer-to-peer (P2P) networks as: “P2P is a class of applications that take advantage of resources storage, cycles, content, human presence available at the edges of the Internet”. As large number of P2P applications, such as Gnutella [1], BitTorrent [2], Skype [3], etc., being popular in Internet, they attract a lot of users and system designers. According to the research of the running P2P applications [4][5][6], a lot of drawbacks of the real P2P systems have been disclosed that performance of the most P2P systems can’t reach or even be proximal to the expectation of users and system designers. The major reason is lacking of the effective cooperation mechanism inherently in the P2P systems, so not all participators can be encouraged to take part in the systems actively and friendly. When a P2P user
This work was supported by 973 Project granted 2003CB314806 and NFSC granted 90204003, and partly supported by 973 Project granted 2006CB701306.
X. Deng and Y. Ye (Eds.): WINE 2005, LNCS 3828, pp. 651–659, 2005. c Springer-Verlag Berlin Heidelberg 2005
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tries to download a file from another user in the same application, he may worry about the virus or attack embedded in that file; the user shares resources with others but who do not; and so on. All of these risks destroy the trust among the system users, so that the users will remind themselves more careful when they take actions in the system full of hazards, which holds back the users’ footstep to cooperate with others. It is an effective solution to construct the reputation system in the P2P networks to build up the trust among the users. But the anonymousness and inherent virtuality of the Internet hamper the implementation of the practical reputation system. In a glimpse of security technologies, it is still difficult to map the user identities of Internet into their social identities uniquely, which means that users can easily get on the Internet with cheap pseudonyms. So the users can reenter the network system by changing their network identities to get new reputation values to avoid the penalty imposed on them, which can’t be identified from the fresh users to the network. We call the former “whitewashers” and “newcomers” for the latter [8]. There is an important problem to be solved that how to limit the whitewashers’ evil behaviors and encourage the newcomers’ beneficial actions through the reputation system, which can make the P2P networks more cooperative and effective. The users’ initial reputation values in the reputation system have impacted their behaviors. To make the P2P networks more cooperative, some researchers [7] have proposed radically to trust all the users initially when they enter the network, so everyone can trade with others rapidly and extensively. But this solution nourishes the whitewashers, which can get new good reputation and do harm to the performance of P2P networks. So others [7] advise conservatively trusting nobody initially at the beginning of users’ participating to the network, which intents to eliminate the whitewashers by imposing the serious penalty on them. But in reality, the useful behaviors of newcomers have also been unfairly restricted in the case of this strategy. It will take newcomers very long time to cumulate enough reputation values to take part in the cooperation in the network, which decreases the network efficiency seriously in the case of P2P users joining and departing the network with high frequency. A new group-based probability initial reputation system has been proposed in this paper to enhance the network cooperative efficiency and block the whitewashers’ destruct behaviors adaptively. When joining the network, every user is assigned a new reputation value based on a probability indicated by a system argument, which is called Initial Trust Probability (abbr. ITP) adjusted according to the behaviors of all the new users. In addition, to avoid the shake of whole network impacted by a few evil users in a certain local, it doesn’t use a globe argument of ITP for new users’ reputation values, but set the one in each local where a group is built up to maintain all the users in coverage of this local. In this group-based reputation system, the impact of whitewasher can be effectively limited in the area of group he belongs to; furthermore, the huge overhead used to maintain the globe argument can be decreased largely and dispersed into every group in network.
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The remainder of this paper is organized as following: Section 2 describes the proposed adaptive group-based reputation system in detail, which is organized into two subsections named group-based reputation architecture and adaptive initial reputation mechanism respectively. Theoretical analysis and simulation results to the performance of the new reputation system are given in section 3 and 4, respectively. Finally, section 5 concludes the paper.
2
An Adaptive Group-Based Reputation System
It is very difficult to distinguish the whitewasher from newcomer in the P2P network, and the impact of whitewasher can not be eliminated completely. If we trust all new users including whitewashers and newcomers without doubt, the efficiency of the network can be improved in the case of fewer whitewashers, but the system runs high risk of attacks with more whitewashers; otherwise, if we don’t trust all the new users completely, the risk can be cut down, but the system cooperative efficiency may be decreased serious especially with fewer whitewashers, so the best solution should be designed based on tradeoff between restricting the whitewasher and encouraging newcomer. Based on the above analysis, we propose an adaptive initial reputation mechanism under the group-based reputation architecture, which attempts to reach that tradeoff. In this method, the new users will be assigned good reputation values with high probability (i.e. Initial Trust Probability, ITP) when there are fewer whitewashers in the P2P networks; as the number of whitewashers has been detected to be increasing, the ITP is decreased corresponding to alleviate the serious impacts of the whitewashers. In order to decrease the overhead used to maintain the globe argument in the whole network and confine the impact of whitewasher within small area, we design a group-based reputation architecture to realize the adaptive initial reputation mechanism, in which every user is organized into an unique group in a certain rule, such as in the rule of physical topology [10], interest-based locality [11][14], etc.. Furthermore, the relationship among the users can also be organized into a hierarchy [12]. In the paper, we design this architecture in the complete distributed style, and each group maintains an individual ITP to assign the reputation to every covered new user. 2.1
Group-Based Reputation Architecture
As showing in the Fig.1, in the group-based reputation architecture, all the users are organized into groups. In simplicity, we assume that one user belong to only one group (in the case of the user belonging to more than one group, the user can be looked as joining the different group with different identity). Each group is assigned a unique identity called group id (GId), and each user in each group is also assigned an unique identity locally called member id (MId), so every user can be identified uniquely by combining his MId with GId. Furthermore, an individual ITP must be maintained consistently by the members in each group. In each group, all the members contribute a part of their storage to cooperatively store and maintain the reputation information set which consists of
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Fig. 1. The group-based reputation architecture in the P2P networks can be organized by taking into account the physical topology, interest locality, and so on
member id (MId), user id (UId), number of action and reputation value for each member. The UId is registered by user to indicate his physical identity such as IP address, MAC address, which can be realized by underlying technologies and used to encourage the user to reenter the network with same UId to get his reserved reputation value. The number of action is used to determine whether the user is new user or old user. The reputation value is the indication of the user’s reliability. This reputation information can be located in the common storage through the distributed hash table (DHT). When the user wants to cooperate with others, he firstly checks the reputation value of cooperator through the group-based reputation system, and then determines whether to do. For example, we consider the cooperation between user A and user B . If two users are in same group, user A can get the reputation information of B through DHT; otherwise if two users are in different groups, user A must search for B’s group and retrieves his reputation information from it. User B does the same as A. At the end of cooperation, user B(A) reports the behavior of A(B) to A(B)’s group members to let them calculate the reputation for A(B) according to it. 2.2
Adaptive Initial Reputation Mechanism
Based on the group-based reputation architecture described above, we farther propose a probability-based adaptive initial reputation mechanism attempting to reach the point of tradeoff between restricting the whitewasher and encouraging newcomer. We set one initial trust probability (ITP) argument which is maintained by all the members in each group, and the group assigns the new user with new reputation value based on the ITP which should be adjusted according to the new user’s behavior. We consider that the whole P2P networks is organized into N groups numbered by Gi = 1, 2, 3 . . . N , and there is an ITP denoted by Pi in each group Gi ,
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further r denotes the user’s reputation value which consists of two parts of value field denoted by [0, Ru ] and [Ru , Rt ], the user is in the state of trust. When a new user A joins a group Gi , the three members with highest reputation value in this group respectively, which means they may work in correct way with highest probability, calculate A’s reputation value TjA based on Pi , then the final reputation value rA is deduced by the member with highest reputation integrating the three calculations. This mechanism can effectively avoid the moral risk of single referee which may judge selfishly based on its own bias. But too many referees may induce the more complexly distributed computing. The method is detailed as following: 1. The three members (referees) in group Gi calculate the reputations for the new user A respectively. • pj = random(0, 1), j = 1, 2, 3 • if (pj Pi ), then TjA = 0, i.e. the new user isn’t trusted by referee j. 2. The referee with highest reputation value calculates the final reputation for the new user A by integrating the above three calculations. • T A = (T1A T2A ) (T2A T3A ) (T1A T3A ), i.e. the new user A will be trusted in the case of at least two referees trusting him. • if (T A = 1), then rA = random(Ru , Rt ); if T A = 0, then rA = random(0, Ru ). The user B reports the behavior of user A to A’s group, then the user with highest reputation adjusts the ITP Pi according to it. The principle to adjust is as following: 1. Pi should be increased as the probability of normal action of the new user; 2. When the new user behaves correctly, Pi should be increased with small span in order to keep the whitewasher within limits; 3. When the new user behaves incorrectly, Pi should be decreased in large span which is scaled as increasing the number of uncooperative behaviors to protect the system from attacks; Furthermore, the ITPs of neighbor groups may be useful for the group, so the group Gi periodically collects its neighbors’ ITPs Pj , j = 1, 2 . . ., and then uses them to calculateits ITP by some weights, for instance, the calculative formula is Pi = wi · Pi + j (wj · Pj ), s.t. wi + j wj = 1, wi , wj > 0. In simplicity, all wj can be same and wi can be set larger in order to make the impacts of the neighbors weaker.
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Performance Evaluation
Before analyzing the performance of this two architectures, we firstly define the mathematical symbols which would be used in the following analysis:
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n pc ptr Pi µn
the total number of users; the average probability of normal behavior of each new user; the real trusted probability of each user; the initial reputation probability of user i’s group; the Average System Utility Gain (ASUG) contributed by one normal action, µn > 0; −µd the ASUG contributed by one abnormal action, µd > 0; The average whole system utility gain is evaluated as following in three different mechanisms. 1. The mechanism of distrusting all the new users All the new users will be distrusted, so it’s difficult for them to cooperate with others, which contributes mostly nothing to the system, so the system gain is U1 = 0. 2. The mechanism of trusting all the new users All the new users are trusted without doubt in this mechanism, so they can fully cooperate with others, which contribute to the performance of system. We calculate the average system utility gain by taking whitewasher and newcomer into account. The average contribution is: 1 [pc · µn + (1 − pc ) · (−µd )] · d(pc ) U2 = n · 0
1
[pc · (µn + µd ) − µd ] · d(pc ) =
=n· 0
1 (µn − µd ) · n 2
(1)
As showing in the Equation.1, if µn > µd , i.e. the gain made by a user’s normal action is bigger than absolute value of the one by his abnormal action, then the ASUG is positive; in the contrary, the ASUG is negative if µn < µd , which disrupt the system performance. 3. Adaptive initial reputation mechanism In this mechanism, ptr of user i is calculated by the mechanism of three referees: ptr = 3 · (1 − Pi ) · Pi2 + Pi3 . Furthermore, Pi should be increased with the pc , denoted as Pi = f (pc ). To be simple, we assume f (pc ) = pc , so the average contribution made by one new user i is: 1 ptr · [pc · µn + (1 − pc ) · (−µd )] · d(pc ) U3 = n · 0
7 3 (µn − · µd ) · n = 20 7
(2)
If µd < 73 ·µn , then the ASUG is positive; otherwise it is negative if µd > 73 ·µn . Comparing the Equation.1 with the Equation.2, the third mechanism can reach the better ASUG when , which condition can be satisfied in general because destruct impact of the abnormal action always is experientially larger than the constructive impact of the normal action.
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Simulation Results
In this simulation, we only concentrate on the effect of adaptive initial reputation mechanism for all new users who will be deleted after its acting for k times during system running. The ITP Pi in each group is adjusted in the following rules: 1. Pi = min{Pi + mPii·k , P }, when the new user acts correctly. mi is the number of users in group Gi , and P is the upper bound of Pi . Pi should be increased in slow step to avoid system collapse easily due to evil attack. 2. Pi = max{P , Pi − a·(1 − Pi )}, 0 < a < 1, when the new user acts incorrectly. a is a coefficient, and P is the lower bound of Pi . When the abnormal action is detected in first time, it is may be occasional, so we can decrease the Pi in small span to keep the degree of the cooperation; and then the decreasing span is increased as the times of the abnormal action, which can limit the abnormal user and protect the system from being attacked. The reputation value of each user is adjusted in the following rules: 1. ri = min{ri + b, R}, when a new user acts correctly. b is a constant and R is the upper bound of the reputation value; 2. ri = min{ri /2, R}, when a new user acts abnormally. R is the lower bound of the reputation value. Table 1. The major parameters and their values in the simulation Parameter
Value
Number of groups N
50
Average number of member in each group mi
50
Probability of joining of a new user
0.5
Probability of abnormal action of a new user pc 0.1 · · · 0.9 Upper bound of reputation value R
100
Lower bound of reputation value R
5
Upper bound of ITP P
0.95
Lower bound of ITP P
0.05
a
0.5
Simulating time
60 s
The major parameters of the simulation are listed in Table.1. And the simulation result is showed in the Fig.2. We can compare the three mechanism in the simulation result as follow: 1. In the case of low probability pc of abnormal action, the total ASUG of “Trust all” has hit the highest point because it incentives all the users to
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Fig. 2. The average system utility gains in the three different initial reputation mechanisms, the “Adaptive” mechanism’s performance is between the other two (µd = µn = 10)
cooperate completely with each other. But as pc increasing, the ASUG of “Trust all” is decreased sharply, and reaches the lowest point because “Trust all” also give the complete freedom to the abnormal user to attack the system and not stop them. 2. Regardless of pc , “Distrust all” has not almost contributed any ASUG because no user can be trusted which prevents the cooperation, so “Distrust all” leads to worst efficiency. 3. In the case of low pc , the performance of “Adaptive” mechanism is significantly better than the “Distrust all”, but worse than the “Trust all” because the “Adaptive” doubt all the new users with some degree, which cannot make all them cooperate fully in this condition; however, in the case of high pc , the “Adaptive” is worse than the “Distrust all”, but largely better than “Trust all” because it restricted a large number of abnormal actions. In totally, the average whole gain of the “Adaptive” is 16% larger than the one of the “Trust all”. Totally, the performance of “Adaptive” is between “Trust all” and “Distrust all”. Ideally, the ASUG of “Adaptive” should be close to that of “Trust all” when pc is low and “Distrust all” when pc is high, because “Adaptive” need to adjust ITP according to users’ action at expense of system utility. But, it can adapt to any value of pc .
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Conclusions
In this paper, we propose a group-based adaptive reputation system to limit the whitewasher’s behaviors and improve the performance of the P2P networks effectively. The globe fluctuation of the P2P networks can be avoided under the group-based reputation architecture where the impact of whitewasher can be limited locally in his group. Through the evaluation and simulation, it is
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demonstrated that the average system utility gain can be improved by at least 16 percent comparing with other two mechanisms. Through the proposed mechanism is proved to be effective, the some parts of it can be further researched in the future, for example, is it reasonable for the method of adjusting the initial reputation probability of group? Is it fair for the rules of calculating the user’s reputation? All these problems have important impacts on the system performance and should be solved more rationally and effectively.
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