Bilevel Optimization for Differentially Private Optimization in Energy Systems

Autor:	Mak, Terrence W. K., Fioretto, Ferdinando, Van Hentenryck, Pascal
Rok vydání:	2020
Předmět:	Mathematics - Optimization and Control Computer Science - Artificial Intelligence Computer Science - Cryptography and Security
Druh dokumentu:	Working Paper
Popis:	This paper studies how to apply differential privacy to constrained optimization problems whose inputs are sensitive. This task raises significant challenges since random perturbations of the input data often render the constrained optimization problem infeasible or change significantly the nature of its optimal solutions. To address this difficulty, this paper proposes a bilevel optimization model that can be used as a post-processing step: It redistributes the noise introduced by a differentially private mechanism optimally while restoring feasibility and near-optimality. The paper shows that, under a natural assumption, this bilevel model can be solved efficiently for real-life large-scale nonlinear nonconvex optimization problems with sensitive customer data. The experimental results demonstrate the accuracy of the privacy-preserving mechanism and showcases significant benefits compared to standard approaches. Comment: 9 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2001.09508 Zobrazit plný text záznamu View this record from Arxiv