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Security and Communication Networks
Volume 2017, Article ID 4842694, 11 pages
Research Article

An Efficient Context-Aware Privacy Preserving Approach for Smartphones

1Ministry of Education Key Laboratory for Modern Teaching Technology, Shaanxi Normal University, Xi’an 710119, China
2School of Computer Science, Shaanxi Normal University, Xi’an 710119, China
3Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA

Correspondence should be addressed to Xiaoming Wang; nc.ude.unns@mxgnaw

Received 11 March 2017; Accepted 12 April 2017; Published 27 April 2017

Academic Editor: Qing Yang

Copyright © 2017 Lichen Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


With the proliferation of smartphones and the usage of the smartphone apps, privacy preservation has become an important issue. The existing privacy preservation approaches for smartphones usually have less efficiency due to the absent consideration of the active defense policies and temporal correlations between contexts related to users. In this paper, through modeling the temporal correlations among contexts, we formalize the privacy preservation problem to an optimization problem and prove its correctness and the optimality through theoretical analysis. To further speed up the running time, we transform the original optimization problem to an approximate optimal problem, a linear programming problem. By resolving the linear programming problem, an efficient context-aware privacy preserving algorithm (CAPP) is designed, which adopts active defense policy and decides how to release the current context of a user to maximize the level of quality of service (QoS) of context-aware apps with privacy preservation. The conducted extensive simulations on real dataset demonstrate the improved performance of CAPP over other traditional approaches.