In this section
@inproceedings{Dvorkin:2025:10.1109/CDC57313.2025.11312534,
author = {Dvorkin, V and Fioretto, F and Van, Hentenryck P and Pinson, P and Kazempour, J},
doi = {10.1109/CDC57313.2025.11312534},
pages = {8149--8156},
title = {Privacy-Preserving Convex Optimization: When Differential Privacy Meets Stochastic Programming},
url = {http://dx.doi.org/10.1109/CDC57313.2025.11312534},
year = {2025}
}
TY - CPAPER
AB - Convex optimization finds many applications where optimization results may expose private data (e.g., health records, commercial information). To guarantee privacy to optimization data owners, we develop a new privacy-preserving perturbation strategy for convex optimization programs by combining stochastic (chance-constrained) programming and differential privacy. Unlike standard noise-additive strategies, which perturb either optimization data or result, we formulate optimization variables as functions of a random perturbation using linear decision rules; we then optimize these rules to accommodate the perturbation within the feasible region using chance constraints. The perturbation becomes feasible and makes adjacent - in the sense of some distance function - optimization datasets statistically similar in randomized optimization results, thereby enabling privacy guarantees.
AU - Dvorkin,V
AU - Fioretto,F
AU - Van,Hentenryck P
AU - Pinson,P
AU - Kazempour,J
DO - 10.1109/CDC57313.2025.11312534
EP - 8156
PY - 2025///
SN - 0743-1546
SP - 8149
TI - Privacy-Preserving Convex Optimization: When Differential Privacy Meets Stochastic Programming
UR - http://dx.doi.org/10.1109/CDC57313.2025.11312534
ER -