2012/10/11 by Pradeep Chathuranga Weeraddana, Weeraddana, Pradeep Chathuranga, George Athanasiou +7 · 1 citation
Computer Science · #Cryptography and Data Security #Cryptography and Security (cs.CR) #Distributed #FOS: Computer and information sciences #Parallel #Privacy-Preserving Technologies in Data #Security in Wireless Sensor Networks #and Cluster Computing (cs.DC) #cs.CR #cs.DC
paper · pdf · doi:10.48550/arxiv.1210.3283
openalex publication_date 2012/10/11 · arxiv created 2014/06/13 · arxiv updated 2014/06/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Ensuring privacy of sensitive data is essential in many contexts, such as healthcare data, banks, e-commerce, wireless sensor networks, and social networks. It is common that different entities coordinate or want to rely on a third party to solve a specific problem. At the same time, no entity wants to publish its problem data during the solution procedure unless there is a privacy guarantee. Unlike cryptography and differential privacy based approaches, the methods based on optimization lack a quantification of the privacy they can provide. The main contribution of this paper is to provide a mechanism to quantify the privacy of a broad class of optimization approaches. In particular, we formally define a one-to-many relation, which relates a given adversarial observed message to an uncertainty set of the problem data. This relation quantifies the potential ambiguity on problem data due to the employed optimization approaches. The privacy definitions are then formalized based on the uncertainty sets. The properties of the proposed privacy measure is analyzed. The key ideas are illustrated with examples, including localization, average consensus, among others.