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Publication Detail
How to share your favourite search results while preserving privacy and quality
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Conference Proceeding
  • Authors:
    Danezis G, Aura T, Chen S, KIcIman E
  • Publication date:
    16/08/2010
  • Pagination:
    273, 290
  • Journal:
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
  • Volume:
    6205 LNCS
  • Status:
    Published
  • Print ISSN:
    0302-9743
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. © 2010 Springer-Verlag Berlin Heidelberg.
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