mlsanityData to 7 Oct 2026

A unified interpretation of the Gaussian mechanism for differential privacy through the sensitivity index

arXiv 2109.10528 ↗First posted 22 Sep 2021cs.CR cs.IT cs.LGcited by 1

Not on the map. Why?

Abstract (arXiv)

The Gaussian mechanism (GM) represents a universally employed tool for achieving differential privacy (DP), and a large body of work has been devoted to its analysis. We argue that the three prevailing interpretations of the GM, namely (varepsilon, δ)-DP, f-DP and R'enyi DP can be expressed by using a single parameter psi, which we term the sensitivity index. psi uniquely characterises the GM and its properties by encapsulating its two fundamental quantities: the sensitivity of the query and the magnitude of the noise perturbation. With strong links to the ROC curve and the hypothesis-testing interpretation of DP, psi offers the practitioner a powerful method for interpreting, comparing and communicating the privacy guarantees of Gaussian mechanisms.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 41 references that are in the map. First posted on arXiv 22 Sep 2021.

Builds on

17 of 41 references.

References · 17

  1. 2016Deep Learning with Differential Privacycited by 2,682
  2. 2017Renyi Differential Privacycited by 643
  3. 2016Concentrated Differential Privacy: Simplifications, Extensions, and Lower Boundscited by 339
  4. 2013The Composition Theorem for Differential Privacycited by 290
  5. 2014RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Responsecited by 262
  6. 2018Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergencescited by 237
  7. 2020Differentially Private Learning Needs Better Features (or Much More Data)cited by 233
  8. 2018Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoisingcited by 216
  9. 2019R'enyi Differential Privacy of the Sampled Gaussian Mechanismcited by 199
  10. 2021Numerical Composition of Differential Privacycited by 174
  11. 2016Concentrated Differential Privacycited by 139
  12. 2019Deep Learning with Gaussian Differential Privacycited by 112
5 more
  1. 2019Hypothesis Testing Interpretations and Renyi Differential Privacycited by 84
  2. 2021Optimal Accounting of Differential Privacy via Characteristic Functioncited by 83
  3. 2017Privacy Loss in Apple's Implementation of Differential Privacy on MacOS 10.12cited by 42
  4. 2016Understanding the Sparse Vector Technique for Differential Privacycited by 27
  5. 2020Three Variants of Differential Privacy: Lossless Conversion and Applicationscited by 25

Built on by

Cited by year

Citing papers per year: 2022: 1.

1 paper.

  1. 2022SoK: Differential Privacy on Graph-Structured Datacited by 9