Differentially Private Convex Optimization with Feasibility Guarantees
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Publication:6343451
arXiv2006.12338MaRDI QIDQ6343451
Author name not available (Why is that?)
Publication date: 22 June 2020
Abstract: This paper develops a novel differentially private framework to solve convex optimization problems with sensitive optimization data and complex physical or operational constraints. Unlike standard noise-additive algorithms, that act primarily on the problem data, objective or solution, and disregard the problem constraints, this framework requires the optimization variables to be a function of the noise and exploits a chance-constrained problem reformulation with formal feasibility guarantees. The noise is calibrated to provide differential privacy for identity and linear queries on the optimization solution. For many applications, including resource allocation problems, the proposed framework provides a trade-off between the expected optimality loss and the variance of optimization results.
Has companion code repository: https://github.com/wdvorkin/DP_CO_FG
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