Differential privacy is widely considered the formal privacy for privacy-preserving data analysis due to its robust and rigorous guarantees, with increasingly broad adoption in public services, academia, and industry. Although differential privacy originated in the cryptographic context, in this review we argue that, fundamentally, it can be considered a pure statistical concept. We leverage Blackwell's informativeness theorem and focus on demonstrating that the definition of differential privacy can be formally motivated from a hypothesis testing perspective, thereby showing that hypothesis testing is not merely convenient but also the right language for reasoning about differential privacy. This insight leads to the definition of f-differential privacy, which extends other differential privacy definitions through a representation theorem. We review techniques that render f-differential privacy a unified framework for analyzing privacy bounds in data analysis and machine learning. Applications of this differential privacy definition to private deep learning, private convex optimization, shuffled mechanisms, and US Census data are discussed to highlight the benefits of analyzing privacy bounds under this framework compared with existing alternatives.

中文翻译：

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差分隐私的统计观点：假设检验、表示和 Blackwell 定理


差分隐私因其强大而严格的保证而被广泛认为是隐私保护数据分析的正式隐私，在公共服务、学术界和工业界得到了越来越广泛的采用。尽管差分隐私起源于密码学背景，但在这篇综述中，我们认为，从根本上说，它可以被认为是一个纯粹的统计概念。我们利用 Blackwell 的信息定理，专注于证明差分隐私的定义可以从假设检验的角度正式启动，从而证明假设检验不仅方便，而且是推理差分隐私的正确语言。这一见解导致了 f-差分隐私的定义，它通过表示定理扩展了其他差分隐私定义。我们回顾了使 f 差分隐私成为数据分析和机器学习中分析隐私边界的统一框架的技术。讨论了这种差分隐私定义在私有深度学习、私有凸优化、洗牌机制和美国人口普查数据中的应用，以强调与现有替代方案相比，在此框架下分析隐私边界的好处。