2011/11/15 by David Rebollo‐Monedero, David Rebollo-Monedero, Rebollo-Monedero, David +8
Computer Science · Mathematics · #Adversarial Robustness in Machine Learning #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Information Theory (cs.IT) #Information and Cyber Security #Privacy-Preserving Technologies in Data #cs.CR #cs.IT #math.IT
paper · pdf · doi:10.48550/arxiv.1111.3567
This paper has 18 pages and 17 figures
openalex publication_date 2011/11/15 · arxiv created 2012/11/13 · arxiv updated 2012/11/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
A wide variety of privacy metrics have been proposed in the literature to evaluate the level of protection offered by privacy enhancing-technologies. Most of these metrics are specific to concrete systems and adversarial models, and are difficult to generalize or translate to other contexts. Furthermore, a better understanding of the relationships between the different privacy metrics is needed to enable more grounded and systematic approach to measuring privacy, as well as to assist systems designers in selecting the most appropriate metric for a given application. In this work we propose a theoretical framework for privacy-preserving systems, endowed with a general definition of privacy in terms of the estimation error incurred by an attacker who aims to disclose the private information that the system is designed to conceal. We show that our framework permits interpreting and comparing a number of well-known metrics under a common perspective. The arguments behind these interpretations are based on fundamental results related to the theories of information, probability and Bayes decision.