Privacy preserving similarity detection for data analysis

Leontiadis, Iraklis; Önen, Melek; Molva, Refik; Chorley, M.J; Colombo, G.B
CSAR 2013, Collective Social Awareness and Relevance Workshop, co-located with the 3rd international conference on Social Computing and its Applications, 30 September-2 October 2013, Karlsruhe, Germany

Current applications tend to use personal sensitive information to achieve better quality with respect to their services. Since the third parties are not trusted the data must be protected
such that individual data privacy is not compromised but at the same time operations on it would be compatible. A wide range of data analysis operations entails a similarity detection algorithm
between user data. For instance clustering on big data groups together objects based on the heuristic that similar objects are likely to be put under the same cluster. Similarity decisions are
important for numerous applications such as: online social networks, recommendations systems and behavioral advertisement. In this paper we propose a mechanism that protects user privacy
and preserves data similarity results although encrypted. We analyze the security of the scheme and we further demonstrate its correctness and feasibility through a real life experiment where "personality traits" by users are collected for a 4square application.

Sécurité numérique
Eurecom Ref:
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