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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 22 (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 10 (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.
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.
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
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
Improved Collaborative Filtering
"... Abstract. We consider the interactive model of collaborative filtering, where each member of a given set of users has a grade for each object in a given set of objects. The users do not know the grades at start, but a user can probe any object, thereby learning her grade for that object directly. We ..."
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Abstract. We consider the interactive model of collaborative filtering, where each member of a given set of users has a grade for each object in a given set of objects. The users do not know the grades at start, but a user can probe any object, thereby learning her grade for that object directly. We describe reconstruction algorithms which generate good estimates of all user grades (“preference vectors”) using only few probes. To this end, the outcomes of probes are posted on some public “billboard”, allowing users to adopt results of probes executed by others. We give two new algorithms for this task under very general assumptions on user preferences: both improve the best known query complexity for reconstruction, and one improving resilience in the presence of many users with esoteric taste. 1