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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| Vydáno v: | Expert systems with applications Ročník 259; s. 125279 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
01.01.2025
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| Témata: | |
| ISSN: | 0957-4174 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | 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. |
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| ISSN: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2024.125279 |