Using MOPSO for Optimizing Randomized Response Schemes in Privacy Computing

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.