Differential Privacy: What is all the noise about?

• URL: http://arxiv.org/abs/2205.09453v1
• Date: Thu, 19 May 2022 10:12:29 GMT
• Title: Differential Privacy: What is all the noise about?
• Authors: Roxana Danger
• Abstract summary: Differential Privacy (DP) is a formal definition of privacy that provides rigorous guarantees against risks of privacy breaches during data processing. This paper aims to provide an overview of the most important ideas, concepts and uses of DP in Machine Learning (ML)
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Differential Privacy (DP) is a formal definition of privacy that provides rigorous guarantees against risks of privacy breaches during data processing. It makes no assumptions about the knowledge or computational power of adversaries, and provides an interpretable, quantifiable and composable formalism. DP has been actively researched during the last 15 years, but it is still hard to master for many Machine Learning (ML)) practitioners. This paper aims to provide an overview of the most important ideas, concepts and uses of DP in ML, with special focus on its intersection with Federated Learning (FL).

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