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DSybil: Optimal SybilResistance for Recommendation Systems
, 2009
"... Recommendation systems can be attacked in various ways, and the ultimate attack form is reached with a sybil attack, where the attacker creates a potentially unlimited number of sybil identities to vote. Defending against sybil attacks is often quite challenging, and the nature of recommendation sys ..."
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Cited by 23 (4 self)
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Recommendation systems can be attacked in various ways, and the ultimate attack form is reached with a sybil attack, where the attacker creates a potentially unlimited number of sybil identities to vote. Defending against sybil attacks is often quite challenging, and the nature of recommendation systems makes it even harder. This paper presents DSybil, a novel defense for diminishing the influence of sybil identities in recommendation systems. DSybil provides strong provable guarantees that hold even under the worstcase attack and are optimal. DSybil can defend against an unlimited number of sybil identities over time. DSybil achieves its strong guarantees by i) exploiting the heavytail distribution of the typical voting behavior of the honest identities, and ii) carefully identifying whether the system is already getting “enough help ” from the (weighted) voters already taken into account or whether more “help ” is needed. Our evaluation shows that DSybil would continue to provide highquality recommendations even when a millionnode botnet uses an optimal strategy to launch a sybil attack. 1.
Tell me who I am: An interactive recommendation system
 In Proc. 18th Ann. ACM Symp. on Parallelism in Algorithms and Architectures
, 2006
"... We consider a model of recommendation systems, where each member from a given set of players has a binary preference to each element in a given set of objects: intuitively, each player either likes or dislikes each object. However, the players do not know their preferences. To find his preference of ..."
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Cited by 11 (6 self)
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We consider a model of recommendation systems, where each member from a given set of players has a binary preference to each element in a given set of objects: intuitively, each player either likes or dislikes each object. However, the players do not know their preferences. To find his preference of an object, a player may probe it, but each probe incurs unit cost. The goal of the players is to learn their complete preference vector (approximately) while incurring minimal cost. This is possible if many players have similar preference vectors: such a set of players with similar “taste ” may split the cost of probing all objects among them, and share the results of their probes by posting them on a public billboard. The problem is that players do not know a priori whose taste is close to theirs. In this paper we present a distributed randomized peertopeer algorithm in which each player outputs a vector which is close to the best possible approximation of the player’s real preference vector after a polylogarithmic number of rounds. constraint. The algorithm works under adversarial preferences. Previous algorithms either made severely limiting assumptions on the structure of the preference vectors, or had polynomial overhead.
Finding similar users in social networks: extended abstract
 In Proc. 21st Ann. ACM Symp. on Parallelism in Algorithms and Architectures (SPAA
, 2009
"... We consider a system where users wish to find similar users. To model similarity, we assume the existence of a set of queries, and two users are deemed similar if their answers to these queries are (mostly) identical. Formally, each user has a vector of preferences, and two users are similar if thei ..."
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We consider a system where users wish to find similar users. To model similarity, we assume the existence of a set of queries, and two users are deemed similar if their answers to these queries are (mostly) identical. Formally, each user has a vector of preferences, and two users are similar if their preference vectors differ in only a few coordinates. The preferences are unknown to the system initially, and the goal of the algorithm is to classify the users into classes of roughly the same preferences with the least possible number of queries presented to any user. We prove nearly matching lower and upper bounds on that problem. Specifically, we present an “anytime ” algorithm that maintains a partition of the users, and the quality of the partition improves over time: let n be the number of users. At time T, groups of Õ (n/T) users with the same preferences will be separated (with high probability) if they differ in sufficiently many queries. We present a lower bound that matches the upper bound, up to a constant factor, for nearly all possible distances between user groups. Classical research in social networks tries to analyze their structure and evolution from the observer point of view [16, 8]. Recently, with the emergence of Internetbased social networks such as Facebook [1], MySpace
Collaborative Scoring with Dishonest Participants ABSTRACT
"... Consider a set of players that are interested in collectively evaluating a set of objects. We develop a collaborative scoring protocol in which each player evaluates a subset of the objects, after which we can accurately predict each players’ individual opinion of the remaining objects. The accuracy ..."
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Cited by 2 (0 self)
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Consider a set of players that are interested in collectively evaluating a set of objects. We develop a collaborative scoring protocol in which each player evaluates a subset of the objects, after which we can accurately predict each players’ individual opinion of the remaining objects. The accuracy of the predictions is near optimal, depending on the number of objects evaluated by each player and the correlation among the players ’ preferences. A key novelty is the ability to tolerate malicious players. Surprisingly, the malicious players cause no (asymptotic) loss of accuracy in the predictions. In fact, our algorithm improves in both performance and accuracy over prior stateoftheart collaborative scoring protocols that provided no robustness to malicious disruption.
Recommender Systems With NonBinary Grades
"... We consider the interactive model of recommender systems, in which users are asked about just a few of their preferences, and in return the system outputs an approximation of all their preferences. The measure of performance is the probe complexity of the algorithm, defined to be the maximal number ..."
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Cited by 1 (1 self)
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We consider the interactive model of recommender systems, in which users are asked about just a few of their preferences, and in return the system outputs an approximation of all their preferences. The measure of performance is the probe complexity of the algorithm, defined to be the maximal number of answers any user should provide (probe complexity typically depends inversely on the number of users with similar preferences and on the quality of the desired approximation). Previous interactive recommendation algorithms assume that user preferences are binary, meaning that each object is either “liked ” or “disliked ” by each user. In this paper we consider the general case in which users may have a more refined scale of preference, namely more than two possible grades. We show how to reduce the nonbinary case to the binary one, proving the following results. For discrete grades with s possible values, we give a simple deterministic reduction that preserves the approximation properties of the binary algorithm at the cost of increasing probe complexity by factor s. Our main result is for the general case, where we assume that user grades are arbitrary real numbers. For this case we present an algorithm that preserves the approximation properties of the binary algorithm while incurring only polylogarithmic overhead. 1.
Addressing Sparsity in Decentralized Recommender Systems through Random Walks
, 2010
"... Abstract. The need for efficient decentralized recommender systems has been appreciated for some time, both for the intrinsic advantages of decentralization and the necessity of integrating recommender systems into existing P2P applications. On the other hand, the accuracy of recommender systems is ..."
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Abstract. The need for efficient decentralized recommender systems has been appreciated for some time, both for the intrinsic advantages of decentralization and the necessity of integrating recommender systems into existing P2P applications. On the other hand, the accuracy of recommender systems is often hurt by data sparsity. In this paper, we compare different decentralized userbased and itembased Collaborative Filtering (CF) algorithms with each other, and propose a new userbased random walk approach customized for decentralized systems, specifically designed to handle sparse data. We show how the application of random walks to decentralized environments is different from the centralized version. We examine the performance of our random walk approach in different settings by varying the sparsity, the similarity measure and the neighborhood size. In addition, we introduce the popularizing disadvantage of the significance weighting term traditionally used to increase the precision of similarity measures, and elaborate how it can affect the performance of the random walk algorithm. The simulations on MovieLens 10,000,000 ratings dataset demonstrate that over a wide range of sparsity, our algorithm outperforms other decentralized CF schemes. Moreover, our results show decentralized userbased approaches perform better than their itembased counterparts in P2P recommender applications. 1
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"... decentralized, collaborative, and personalized recommendation system. We examine a voting scheme where users have to assess a continuous stream of items to be either good or bad—either manually or, assisted by TROOTH, automatically based on previous votes. For this purpose, TROOTH implicitly creates ..."
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decentralized, collaborative, and personalized recommendation system. We examine a voting scheme where users have to assess a continuous stream of items to be either good or bad—either manually or, assisted by TROOTH, automatically based on previous votes. For this purpose, TROOTH implicitly creates special interest groups containing users who share similar opinions expressed by assenting votes. To evaluate an item with TROOTH, a user trusts those votes most which have been cast by other users in the same group. TROOTH has been implemented in the SPAMATO spam filter system where it is used in the context of collaborative spam filtering. 1
A Novel Protocol for Communicating Reputation in P2P Networks
"... Abstract. Many reputation systems mainly focus on avoiding untrustworthy agents by communicating reputation. Here arises the problem that when an agent is not ignorant of another then there is no way to notice ambiguity. This paper shows a new protocol in which an agent can measure ambiguity using t ..."
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Abstract. Many reputation systems mainly focus on avoiding untrustworthy agents by communicating reputation. Here arises the problem that when an agent is not ignorant of another then there is no way to notice ambiguity. This paper shows a new protocol in which an agent can measure ambiguity using the notion of statistics, and illustrates the method of designing agents ’ algorithms as well as existing reputation systems. 1
Development, Deployment, and Rating of PlugIns Technical Report TIK259
, 2006
"... In this paper, we present a lightweight but powerful plugin container which provides advanced features such as dynamic class loading, dependency, configuration, and security management. We highlight the deployment mechanism which allows to publish, install, and update plugins from arbitrary source ..."
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In this paper, we present a lightweight but powerful plugin container which provides advanced features such as dynamic class loading, dependency, configuration, and security management. We highlight the deployment mechanism which allows to publish, install, and update plugins from arbitrary sources at runtime. Furthermore, we introduce the Trooth voting and trust system which is used to assess plugins but has been designed as a general rating mechanism. Finally, we present the extensible architecture of the Spamato spam filter framework and show how it employs the plugin mechanism and the Trooth system in a realworld application. 1
Asynchronous Recommendation Systems EXTENDED ABSTRACT
, 2007
"... We consider the following abstraction of recommendation systems. There are n players and m objects, and each player has an arbitrary binary preference grade (“likes ” or “dislikes”) for each object. The problem is that these preferences are not known, and the goal of the players is to discover their ..."
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We consider the following abstraction of recommendation systems. There are n players and m objects, and each player has an arbitrary binary preference grade (“likes ” or “dislikes”) for each object. The problem is that these preferences are not known, and the goal of the players is to discover their own preferences. To do that, a player can probe each object, thereby directly finding his preference grade for the objects. However, probing an object incurs cost. To save on cost, players post the results of their probes on a public billboard: writing and reading from the billboard is free. The idea is that cost can be reduced if players with similar preferences share the load of probing, but such similarities are not a priori known to the players. In a synchronous recommendation system, players probe in global rounds, and in an asynchronous system, players probe in an order determined by an arbitrary schedule. In this paper we present the first asynchronous recommendation systems that can reconstruct the preferences of players under adversarial asynchronous scheduling, with polylogarithmic overhead in cost with respect to the best possible. We present algorithms both for exact and approximate preference reconstructions.