Computing Trusted Authority Scores in Peer-to-Peer Web Search Networks

Parreira, Josiane Xavier; Donato, Debora; Castillo, Carlos; Weikum, Gerhard

doi:10.1145/1244408.1244422

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Computing Trusted Authority Scores in Peer-to-Peer Web Search Networks

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Parreira, Josiane Xavier
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Weikum, Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Citation

Parreira, J. X., Donato, D., Castillo, C., & Weikum, G. (2007). Computing Trusted Authority Scores in Peer-to-Peer Web Search Networks. In C. Castillo, K. Chellapilla, & B. Davison (Eds.), AIRWeb 2007: Proceedings of the 3rd International Workshop on Adversarial Information Retrieval on the Web (pp. 73-80). New York, NY, USA: ACM.

Cite as: https://hdl.handle.net/11858/00-001M-0000-000F-1EAE-C

Abstract

Peer-to-peer ({P2P}) networks have received great attention for sharing and searching information in large user communities. The open and anonymous nature of {P2P} networks is one of its main strengths, but it also opens doors to manipulation of the information and of the quality ratings. In our previous work (J. X. Parreira, D. Donato, S. Michel and G. Weikum in {VLDB} 2006) we presented the {JXP} algorithm for distributed computing {P}age{R}ank scores for information units (Web pages, sites, peers, social groups, etc.) within a link- or endorsement-based graph structure. The algorithm builds on local authority computations and bilateral peer meetings with exchanges of small data structures that are relevant for gradually learning about global properties and eventually converging towards global authority rankings. In the current paper we address the important issue of cheating peers that attempt to distort the global authority values, by providing manipulated data during the peer meetings. Our approach to this problem enhances {JXP} with statistical techniques for detecting suspicious behavior. Our method, coined {T}rust{JXP}, is again completely decentralized, and we demonstrate its viability and robustness in experiments with real {W}eb data.