Leveraging node properties in random walks for robust reputations in decentralized networks

Gkorou, Dimitra; Vinkó, Tamás; Pouwelse, Johan; Epema, Dick

doi:10.1109/p2p.2013.6688708

Cited by 6 publications

(11 citation statements)

References 24 publications

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“…The Bartercast graph is derived from the distributed reputation mechanism called Bartercast [11] of the BitTorrent-based client Tribler [20]. This dataset contains information about 29,716 nodes and their interactions [14]. The weights of the edges represent the amount of data (in KB) transferred between two users.…”

Section: Experiments Methodologymentioning

confidence: 99%

“…In EscapeLimit, we use random walks with restarts [23], where a random walk is directed back towards its initiator with a fixed restart probability. A random walk with restarts represents better the inherent trust in a network, since each node trusts itself more than the other nodes and its trust towards the other nodes decays with the increase of their distance [14,17]. Each node in the network performs its own random walks and requests parts of the interaction subgraphs of the contacted nodes.…”

Section: Collecting Information Using Random Walksmentioning

confidence: 99%

“…Unlike previously proposed methods, in order to incorporate trust we use the interaction subgraph available at each node, as regular and successful interactions between nodes are strong indicators of trust relationships and similarity among users [14], [17]. In many proposed systems such as SybilRank [7] and SybilInfer [9], random walks enhance their resilience against sybil attacks by using a social network to incorporate trust.…”

Section: Collecting Information Using Random Walksmentioning

confidence: 99%

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Trust-based collection of information in distributed reputation networks

Gkorou¹,

Pouwelse²,

Epema³

2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

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Distributed reputation systems establish trust among strangers in online communities and provide incentives for users to contribute. In these systems, each user monitors the interactions of others and computes the reputations accordingly. Collecting information for computing the reputations is challenging for the users due to their vulnerability to attacks, their limited resources, and the burst of their interactions. The low cost of creating accounts in most reputation systems makes them popular to million of users, but also enables malicious users to boost their reputations by performing Sybil attacks. Furthermore, the burst of user interactions causes an information overload. To avoid the impact of malicious users and information overload, we propose EscapeLimit, a sybil attackresistant, computationally simple, and fully distributed method for information collection. EscapeLimit leverages user interactions as indicators of trust and similarity between the corresponding users, and collects relevant and trusted information by limiting the escape probability into the Sybil area. We evaluate it by emulating interaction patterns derived from synthetic and real-world networks. Our evaluation shows EscapeLimit's effectiveness in terms of its resilience to Sybil attacks, its scalability, and its ability to provide relevant information to each user.

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Section: Experiments Methodologymentioning

confidence: 99%

Section: Collecting Information Using Random Walksmentioning

confidence: 99%

Section: Collecting Information Using Random Walksmentioning

confidence: 99%

See 1 more Smart Citation

Trust-based collection of information in distributed reputation networks

Gkorou¹,

Pouwelse²,

Epema³

2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

Self Cite

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“…Similarly, we get 8 Eqs. (8) and (9) are complicated functions of the amounts p(x), n(x), p(y), n(y). We show that they do not have a clear and well-defined interpretation in terms of evidence handling.…”

Section: Counter-intuitive Behaviour Of the ⊗ Operationmentioning

confidence: 99%

Flow-based reputation with uncertainty: evidence-based subjective logic

źKorić

Hoogh

Zannone

2015

Int. J. Inf. Secur.

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The concept of reputation is widely used as a measure of trustworthiness based on ratings from members in a community. The adoption of reputation systems, however, relies on their ability to capture the actual trustworthiness of a target. Several reputation models for aggregating trust information have been proposed in the literature. The choice of model has an impact on the reliability of the aggregated trust information as well as on the procedure used to compute reputations. Two prominent models are flow-based reputation (e.g., EigenTrust, PageRank) and subjective logic-based reputation. Flow-based models provide an automated method to aggregate trust information, but they are not able to express the level of uncertainty in the information. In contrast, subjective logic extends probabilistic models with an explicit notion of uncertainty, but the calculation of reputation depends on the structure of the trust network and often requires information to be discarded. These are severe drawbacks. In this work, we observe that the 'opinion discounting' operation in subjective logic has a number of basic problems. We resolve these problems by providing a new discounting operator that describes the flow of evidence from one party to another. The adoption of our discounting rule results in a consistent subjective logic algebra that is entirely based on the handling of evidence. We show that the new algebra enables the construction of an automated reputation assessment procedure for arbitrary trust networks, where the calculation no longer depends on the structure of the network, and does not need to throw away any information. Thus, we obtain the best of B Nicola Zannone n.zannone@tue.nl both worlds: flow-based reputation and consistent handling of uncertainties.

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“…PageRank has numerous applications in information retrieval [22,31,37], reputation systems [21,26], machine learning [4,5], and graph partitioning [1,12]. A large complex network can often be conveniently modeled by a random graph.…”

Section: Introductionmentioning

confidence: 99%

PageRank in Undirected Random Graphs

Avrachenkov

Kadavankandy²,

Prokhorenkova³

et al. 2015

Lecture Notes in Computer Science

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Abstract:PageRank has numerous applications in information retrieval, reputation systems, machine learning, and graph partitioning. In this paper, we study PageRank in undirected random graphs with an expansion property. The Chung-Lu random graph is an example of such a graph. We show that in the limit, as the size of the graph goes to infinity, PageRank can be approximated by a mixture of the restart distribution and the vertex degree distribution. We also extend the result to Stochastic Block Model (SBM) graphs, where we show that there is a correction term that depends on the community partitioning.

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Leveraging node properties in random walks for robust reputations in decentralized networks

Cited by 6 publications

References 24 publications

Trust-based collection of information in distributed reputation networks

Trust-based collection of information in distributed reputation networks

Flow-based reputation with uncertainty: evidence-based subjective logic

PageRank in Undirected Random Graphs

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