# Log-Concave and Multivariate Canonical Noise Distributions for Differential Privacy

arXiv: https://arxiv.org/abs/2206.04572 · HTML: https://mlsanity.com/paper/2206.04572/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2022.

## Built on by (most cited first, 5 of 5)

- [Shifted Interpolation for Differential Privacy](https://mlsanity.com/paper/2403.00278/) (2024)
- [A Statistical Viewpoint on Differential Privacy: Hypothesis Testing, Representation and Blackwell's Theorem](https://mlsanity.com/paper/2409.09558/) (2024)
- [Gaussian Certified Unlearning in High Dimensions: A Hypothesis Testing Approach](https://mlsanity.com/paper/2510.13094/) (2025)
- [Beyond Laplace and Gaussian: Exploring the Generalized Gaussian Mechanism for Private Machine Learning](https://mlsanity.com/paper/2506.12553/) (2025)
- [Near-Optimal Private Tests for Simple and MLR Hypotheses](https://mlsanity.com/paper/2601.21959/) (2026)

## Abstract (arXiv)

A canonical noise distribution (CND) is an additive mechanism designed to satisfy f-differential privacy (f-DP), without any wasted privacy budget. f-DP is a hypothesis testing-based formulation of privacy phrased in terms of tradeoff functions, which captures the difficulty of a hypothesis test. In this paper, we consider the existence and construction of both log-concave CNDs and multivariate CNDs. Log-concave distributions are important to ensure that higher outputs of the mechanism correspond to higher input values, whereas multivariate noise distributions are important to ensure that a joint release of multiple outputs has a tight privacy characterization. We show that the existence and construction of CNDs for both types of problems is related to whether the tradeoff function can be decomposed by functional composition (related to group privacy) or mechanism composition. In particular, we show that pure ε-DP cannot be decomposed in either way and that there is neither a log-concave CND nor any multivariate CND for ε-DP. On the other hand, we show that Gaussian-DP, (0,δ)-DP, and Laplace-DP each have both log-concave and multivariate CNDs.