On the Privacy of Optimization
Pradeep Chathuranga Weeraddana, Carlo Fischione · IFAC-PapersOnLine · 2017
In distributed or multiparty computations, optimization theory methods offer appealing privacy properties compared to cryptography and differential privacy methods. However, unlike cryptography and differential privacy, optimization methods currently lack a formal quantification of the privacy they can provide. The main contribution of this paper is to propose a quantification of the privacy of a broad class of optimization approaches. The optimization procedures generate a problem’s data ambiguity for an adversarial observer, which thus observes the problem’s data within an uncertainty set. We formally define a one-to-many relation between a given adversarial observed message and an uncertainty set of the problem’s data. Based on the uncertainty set, a privacy measure is then formalized. The properties of the proposed privacy measure are analyzed. The key ideas are illustrated with examples, including localization and average consensus.