论文标题
差异隐私：什么噪音是什么？
Differential Privacy: What is all the noise about?
论文作者
论文摘要
差异隐私（DP）是对隐私的正式定义，可为数据处理过程中的隐私漏洞提供严格的保证。它没有对对手的知识或计算能力做出任何假设，并提供了一种可解释的，可量化的和综合的形式主义。在过去的15年中，DP已经积极研究，但是对于许多机器学习（ML））从业人员来说仍然很难掌握。本文旨在概述ML中DP最重要的思想，概念和用途，特别关注其与联合学习（FL）的交集。
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).
