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How to Share Your Favourite Search Results while Preserving Privacy and Quality

  • George Danezis
  • Tuomas Aura
  • Shuo Chen
  • Emre Kıcıman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6205)

Abstract

Personalised social search is a promising avenue to increase the relevance of search engine results by making use of recommendations made by friends in a social network. More generally a whole class of systems take user preferences, aggregate and process them, before providing a view of the result to others in a social network. Yet, those systems present privacy risks, and could be used by spammers to propagate their malicious preferences. We present a general framework to preserve privacy while maximizing the benefit of sharing information in a social network, as well as a concrete proposal making use of cohesive social group concepts from social network analysis. We show that privacy can be guaranteed in a k-anonymity manner, and disruption through spam is kept to a minimum in a real world social network.

Keywords

Social Network Target Group Social Network Analysis Social Graph Sybil Attack 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • George Danezis
    • 1
  • Tuomas Aura
    • 2
  • Shuo Chen
    • 1
  • Emre Kıcıman
    • 1
  1. 1.Microsoft ResearchOne Microsoft WayRedmondU.S.
  2. 2.Helsinki University of TechnologyTKKFinland

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