讲清差分隐私的核心机制与应用基础，为AI隐私保护铺路。
Differential Privacy Overview and Fundamental Techniques
- 从失败的隐私保护尝试出发，定义差分隐私的关键要求。
- 系统梳理差分隐私的组合性、后处理免疫等核心性质。
- 总结纯差分隐私与近似差分隐私的基础实现技术。
本章作为《人工智能中的差分隐私：从理论到实践》一书的一部分，介绍差分隐私的基本概念。首先通过分析各类数据隐私保护方法的失败案例，阐明构建稳健隐私定义的关键需求。接着明确隐私保护数据分析中的核心参与者、任务与范围。随后正式定义差分隐私及其固有属性，包括组合性、后处理免疫和群体隐私。最后回顾实现纯差分隐私与近似差分隐私的基本技术与机制。
原文摘要 · Abstract (English)
This chapter is meant to be part of the book "Differential Privacy in Artificial Intelligence: From Theory to Practice" and provides an introduction to Differential Privacy. It starts by illustrating various attempts to protect data privacy, emphasizing where and why they failed, and providing the key desiderata of a robust privacy definition. It then defines the key actors, tasks, and scopes that make up the domain of privacy-preserving data analysis. Following that, it formalizes the definition of Differential Privacy and its inherent properties, including composition, post-processing immunity, and group privacy. The chapter also reviews the basic techniques and mechanisms commonly used to implement Differential Privacy in its pure and approximate forms.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。