Robust incentive techniques for peer-to-peer networks

By Michal Feldman, Kevin Lai, Ion Stoica, John Chung-I Chuang. ACM Conference on Economics and Computation, 2004

654 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Management Science and Operations Research, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/646c1fe897c5f3978a869950a277fb29df61095e

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In Computer Networks and Communications

In Management Science and Operations Research

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Citations in the corpus

Taxonomy of trust: Categorizing P2P reputation systems (2006, background): “Information collected through transitive trust may be weighted by the reputation rating of the least reputable peer in the trust chain [19].”
Overcoming free-riding behavior in peer-to-peer systems (2005, methodology): “[ Feldman et al., 2004a ] propose to apply the maxo w algorithm on the graph.”
A survey of attack and defense techniques for reputation systems (2009, background): “Attackers that are also interacting with other identities in honest ways are known as moles [Feldman et al. 2004].”
Sybilproof reputation mechanisms (2005, background): “Many flow variants are included in this class, such as maximum generalized flow (the case where flow leaks along an edge), or the variation on max flow preferring closer nodes used in [3].”
A Survey of Solutions to the Sybil Attack (2006, neutral): “[74] among others [3, 10,11,13,14,17,27,31,34,37,45,52,60,64,68,73]”
SybilGuard: Defending Against Sybil Attacks via Social Networks (2006, background): “Trust propagation or transitive trust is a technique that researchers often use on trust networks [7, 10, 20, 24].”
Game Theory in Wireless and Communication Networks (2008, background): “In a common approach [146], a peer uploads file to other peers, but when many free riders (i.”
Free-riding and whitewashing in peer-to-peer systems (2004, result): “The penalty mechanism assumes that free-riding behavior is observable, even though innate user types may not be; that is, users are labeled as either contributors or free-riders, and being a free-rider entails a penalty—deterioration of a user’s benefits by a fraction of . While this assumption may ”