Differentially Private Convex Optimization with Feasibility Guarantees

22 Jun 2020Vladimir DvorkinFerdinando FiorettoPascal Van HentenryckJalal KazempourPierre Pinson

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... (read more)

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