A Novel Adaptive Differential Privacy Algorithm for Empirical Risk Minimization

Kaili Zhang (), Haibin Zhang (), Pengfei Zhao () and Haibin Chen
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Kaili Zhang: Department of Operations Research and Information Engineering, Beijing University of Technology, Beijing 100124, P. R. China
Haibin Zhang: Department of Operations Research and Information Engineering, Beijing University of Technology, Beijing 100124, P. R. China
Pengfei Zhao: School of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing 102616, P. R. China
Haibin Chen: School of Management Science, Qufu Normal University, Rizhao Shandong 276800, P. R. China

Asia-Pacific Journal of Operational Research (APJOR), 2021, vol. 38, issue 05, 1-26

Abstract: Privacy-preserving empirical risk minimization model is crucial for the increasingly frequent setting of analyzing personal data, such as medical records, financial records, etc. Due to its advantage of a rigorous mathematical definition, differential privacy has been widely used in privacy protection and has received much attention in recent years of privacy protection. With the advantages of iterative algorithms in solving a variety of problems, like empirical risk minimization, there have been various works in the literature that target differentially private iteration algorithms, especially the adaptive iterative algorithm. However, the solution of the final model parameters is imprecise because of the vast privacy budget spending on the step size search. In this paper, we first proposed a novel adaptive differential privacy algorithm that does not require the privacy budget for step size determination. Then, through the theoretical analyses, we prove that our proposed algorithm satisfies differential privacy, and their solutions achieve sufficient accuracy by infinite steps. Furthermore, numerical analysis is performed based on real-world databases. The results indicate that our proposed algorithm outperforms existing algorithms for model fitting in terms of accuracy.

Keywords: Differential privacy; empirical risk minimization; iteration algorithm (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1142/S021759592140011X

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