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Mathematical Problems in Engineering
Volume 2018, Article ID 7846547, 16 pages
Research Article

Using MOPSO for Optimizing Randomized Response Schemes in Privacy Computing

Department of Information Engineering, Engineering University of Chinese People’s Armed Police Force, Xi'an, China

Correspondence should be addressed to Zhiqiang Gao; moc.qq@4648930901

Received 17 November 2017; Revised 6 February 2018; Accepted 22 February 2018; Published 3 April 2018

Academic Editor: Khaled Loukhaoukha

Copyright © 2018 Zhiqiang Gao 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.


It is a challenging concern in data collecting, publishing, and mining when personal information is controlled by untrustworthy cloud services with unpredictable risks for privacy leakages. In this paper, we formulate an information-theoretic model for privacy protection and present a concrete solution to theoretical architecture in privacy computing from the perspectives of quantification and optimization. Thereinto, metrics of privacy and utility for randomized response (RR) which satisfy differential privacy are derived as average mutual information and average distortion rate under the information-theoretic model. Finally, a discrete multiobjective particle swarm optimization (MOPSO) is proposed to search optimal RR distorted matrices. To the best of our knowledge, our proposed approach is the first solution to optimize RR distorted matrices using discrete MOPSO. In detail, particles’ position and velocity are redefined in the problem-guided initialization and velocity updating mechanism. Two mutation strategies are introduced to escape from local optimum. The experimental results illustrate that our approach outperforms existing state-of-the-art works and can contribute optimal Pareto solutions of extensive RR schemes to future study.