Alternating minimization differential privacy protection algorithm for the novel dual-mode learning tasks model

The privacy-protected algorithm (PPA) is pivotal in the realm of machine learning, especially for handling sensitive data types, such as medical and financial records. PPA enables two distinct operations: data publishing and data analysis, each capable of functioning independently. However, the fiel...

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Bibliographic Details
Published in:Expert systems with applications Vol. 259; p. 125279
Main Authors: Zhao, Pengfei, Zhang, Kaili, Zhang, Haibin, Chen, Haibin
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.01.2025
Subjects:
Alternating minimization algorithm
Differential privacy protection
Dual-mode empirical risk minimization model
Dual-mode learning
Dual-mode empirical risk minimization model
Dual-mode learning
Differential privacy protection
Alternating minimization algorithm
ISSN:0957-4174
Online Access:Get full text
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Summary:The privacy-protected algorithm (PPA) is pivotal in the realm of machine learning, especially for handling sensitive data types, such as medical and financial records. PPA enables two distinct operations: data publishing and data analysis, each capable of functioning independently. However, the field lacks a unified framework or an efficient algorithm to synergize these operations. This deficiency inspires our current research endeavor. In this paper, we introduce a novel dual-mode empirical risk minimization (D-ERM) model, specifically designed for integrated learning tasks. We also develop an alternating minimization differential privacy protection algorithm (AMDPPA) for implementing the D-ERM model. Our theoretical analysis confirms the differential privacy and accuracy of AMDPPA. We validate the algorithm’s efficacy through numerical experiments using real-world datasets, demonstrating its ability to effectively balance privacy with learning efficiency.
ISSN:0957-4174
DOI:10.1016/j.eswa.2024.125279