Differentially Private Convex Optimization with Feasibility Guarantees
It addresses privacy concerns in optimization for applications like resource allocation, though it appears incremental by building on existing differential privacy methods with a focus on constraint handling.
The paper tackles the problem of solving convex optimization with sensitive data and complex constraints while ensuring differential privacy, by introducing a noise-based framework that guarantees feasibility and trades off optimality loss against result variance.
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.