Differential Privacy Overview and Fundamental Techniques
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 34 references that are in the map. First posted on arXiv 7 Nov 2024.
Builds on
6 of 34 references.
References · 6
- 2020Extracting Training Data from Large Language Modelscited by 2,144
- 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
- 2019Deep Leakage from Gradientscited by 1,004
- 2018Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoisingcited by 216
- 2022Differential Privacy and Fairness in Decisions and Learning Tasks: A Surveycited by 47
- 2022Census TopDown: The Impacts of Differential Privacy on Redistrictingcited by 3
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Citing papers per year: 2025: 1.
1 paper.