# R2DP: A Universal and Automated Approach to Optimizing the Randomization Mechanisms of Differential Privacy for Utility Metrics with No Known Optimal Distributions

arXiv: https://arxiv.org/abs/2009.09451 · HTML: https://mlsanity.com/paper/2009.09451/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2020.

## Built on by (most cited first, 8 of 8)

- [Inf2Guard: An Information-Theoretic Framework for Learning Privacy-Preserving Representations against Inference Attacks](https://mlsanity.com/paper/2403.02116/) (2024)
- [UniCR: Universally Approximated Certified Robustness via Randomized Smoothing](https://mlsanity.com/paper/2207.02152/) (2022)
- [Label Inference Attack against Split Learning under Regression Setting](https://mlsanity.com/paper/2301.07284/) (2023)
- [S2NeRF: Privacy-preserving Training Framework for NeRF](https://mlsanity.com/paper/2409.01661/) (2024)
- [Universally Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergence](https://mlsanity.com/paper/2407.14710/) (2024)
- [PLRV-O: Advancing Differentially Private Deep Learning via Privacy Loss Random Variable Optimization](https://mlsanity.com/paper/2509.06264/) (2025)
- [LMO-DP: Optimizing the Randomization Mechanism for Differentially Private Fine-Tuning (Large) Language Models](https://mlsanity.com/paper/2405.18776/) (2024)
- [RDP: Ranked Differential Privacy for Facial Feature Protection in Multiscale Sparsified Subspace](https://mlsanity.com/paper/2408.00294/) (2024)

## Abstract (arXiv)

Differential privacy (DP) has emerged as a de facto standard privacy notion for a wide range of applications. Since the meaning of data utility in different applications may vastly differ, a key challenge is to find the optimal randomization mechanism, i.e., the distribution and its parameters, for a given utility metric. Existing works have identified the optimal distributions in some special cases, while leaving all other utility metrics (e.g., usefulness and graph distance) as open problems. Since existing works mostly rely on manual analysis to examine the search space of all distributions, it would be an expensive process to repeat such efforts for each utility metric. To address such deficiency, we propose a novel approach that can automatically optimize different utility metrics found in diverse applications under a common framework. Our key idea that, by regarding the variance of the injected noise itself as a random variable, a two-fold distribution may approximately cover the search space of all distributions. Therefore, we can automatically find distributions in this search space to optimize different utility metrics in a similar manner, simply by optimizing the parameters of the two-fold distribution. Specifically, we define a universal framework, namely, randomizing the randomization mechanism of differential privacy (R2DP), and we formally analyze its privacy and utility. Our experiments show that R2DP can provide better results than the baseline distribution (Laplace) for several utility metrics with no known optimal distributions, whereas our results asymptotically approach to the optimality for utility metrics having known optimal distributions. As a side benefit, the added degree of freedom introduced by the two-fold distribution allows R2DP to accommodate the preferences of both data owners and recipients.