Cooperative privacy game: a novel strategy for preserving privacy in data publishing

Abstract

Achieving data privacy before publishing has been becoming an extreme concern of researchers, individuals and service providers. A novel methodology, Cooperative Privacy Game (CoPG), has been proposed to achieve data privacy in which Cooperative Game Theory is used to achieve the privacy and is named as Cooperative Privacy (CoP). The core idea of CoP is to play the best strategy for a player to preserve his privacy by himself which in turn contributes to preserving other players privacy. CoP considers each tuple as a player and tuples form coalitions as described in the procedure. The main objective of the CoP is to obtain individuals (player) privacy as a goal that is rationally interested in other individuals’ (players) privacy. CoP is formally defined in terms of Nash equilibria, i.e., all the players are in their best coalition, to achieve k-anonymity. The cooperative values of the each tuple are measured using the characteristic function of the CoPG to identify the coalitions. As the underlying game is convex; the algorithm is efficient and yields high quality coalition formation with respect to intensity and disperse. The efficiency of anonymization process is calculated using information loss metric. The variations of the information loss with the parameters $$\alpha$$ α (weight factor of nearness) and $$\beta$$ β (multiplicity) are analyzed and the obtained results are discussed.

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