A Game Theoretic Approach to Optimize Identity Exposure in Pervasive Computing Environments

A Game Theoretic Approach to Optimize Identity Exposure in Pervasive Computing Environments

Feng W. Zhu (The University of Alabama in Huntsville, USA), Sandra Carpenter (The University of Alabama in Huntsville, USA), Wei Zhu (Intergraph Co., USA) and Matt Mutka (Michigan State University, USA)
Copyright: © 2012 |Pages: 20
DOI: 10.4018/978-1-61350-323-2.ch211
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Abstract

In pervasive computing environments, personal information is typically expressed in digital forms. Daily activities and personal preferences with regard to pervasive computing applications are easily associated with personal identities. Privacy protection is a serious challenge. The fundamental problem is the lack of a mechanism to help people expose appropriate amounts of their identity information when accessing pervasive computing applications. In this paper, the authors propose the Hierarchical Identity model, which enables the expression of one’s identity information ranging from precise detail to vague identity information. The authors model privacy exposure as an extensive game. By finding subgame perfect equilibria in the game, the approach achieves optimal exposure. It finds the most general identity information that a user should expose and which the service provider would accept. The authors’ experiments show that their models can reduce unnecessary identity exposure effectively.

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