Computer Science > Information Theory
arXiv:2405.00616 (cs)
[Submitted on 1 May 2024]
Title:An Expectation-Maximization Relaxed Method for Privacy Funnel
View a PDF of the paper titled An Expectation-Maximization Relaxed Method for Privacy Funnel, by Lingyi Chen and 5 other authors
View PDFHTML (experimental)Abstract:The privacy funnel (PF) gives a framework of privacy-preserving data release, where the goal is to release useful data while also limiting the exposure of associated sensitive information. This framework has garnered significant interest due to its broad applications in characterization of the privacy-utility tradeoff. Hence, there is a strong motivation to develop numerical methods with high precision and theoretical convergence guarantees. In this paper, we propose a novel relaxation variant based on Jensen's inequality of the objective function for the computation of the PF problem. This model is proved to be equivalent to the original in terms of optimal solutions and optimal values. Based on our proposed model, we develop an accurate algorithm which only involves closed-form iterations. The convergence of our algorithm is theoretically guaranteed through descent estimation and Pinsker's inequality. Numerical results demonstrate the effectiveness of our proposed algorithm.
| Subjects: | Information Theory (cs.IT) |
| Cite as: | arXiv:2405.00616 [cs.IT] |
| (orarXiv:2405.00616v1 [cs.IT] for this version) | |
| https://doi.org/10.48550/arXiv.2405.00616 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled An Expectation-Maximization Relaxed Method for Privacy Funnel, by Lingyi Chen and 5 other authors
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