# A Variational Approach to Privacy and Fairness

arXiv: https://arxiv.org/abs/2006.06332 · HTML: https://mlsanity.com/paper/2006.06332/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2020.

## Built on by (most cited first, 14 of 14)

- [Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation](https://mlsanity.com/paper/2101.04108/) (2021)
- [Enforcing fairness in private federated learning via the modified method of differential multipliers](https://mlsanity.com/paper/2109.08604/) (2021)
- [Bottlenecks CLUB: Unifying Information-Theoretic Trade-offs Among Complexity, Leakage, and Utility](https://mlsanity.com/paper/2207.04895/) (2022)
- [Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learning](https://mlsanity.com/paper/2203.12748/) (2022)
- [Variational Leakage: The Role of Information Complexity in Privacy Leakage](https://mlsanity.com/paper/2106.02818/) (2021)
- [Renyi Fair Information Bottleneck for Image Classification](https://mlsanity.com/paper/2203.04950/) (2022)
- [An Efficient Difference-of-Convex Solver for Privacy Funnel](https://mlsanity.com/paper/2403.04778/) (2024)
- [Classification Utility, Fairness, and Compactness via Tunable Information Bottleneck and R'enyi Measures](https://mlsanity.com/paper/2206.10043/) (2022)
- [Privacy for Fairness: Information Obfuscation for Fair Representation Learning with Local Differential Privacy](https://mlsanity.com/paper/2402.10473/) (2024)
- [Generating Synthetic Fair Syntax-agnostic Data by Learning and Distilling Fair Representation](https://mlsanity.com/paper/2408.10755/) (2024)
- [Debiasing Graph Representation Learning based on Information Bottleneck](https://mlsanity.com/paper/2409.01367/) (2024)
- [Learning Fair Graph Representations with Multi-view Information Bottleneck](https://mlsanity.com/paper/2510.25096/) (2025)
- [Nearest-Neighbor Density Estimation for Dependency Suppression](https://mlsanity.com/paper/2603.04224/) (2026)
- [Trustworthy Representation Learning via Information Funnels and Bottlenecks](https://mlsanity.com/paper/2211.01446/) (2022)

## Abstract (arXiv)

In this article, we propose a new variational approach to learn private and/or fair representations. This approach is based on the Lagrangians of a new formulation of the privacy and fairness optimization problems that we propose. In this formulation, we aim to generate representations of the data that keep a prescribed level of the relevant information that is not shared by the private or sensitive data, while minimizing the remaining information they keep. The proposed approach (i) exhibits the similarities of the privacy and fairness problems, (ii) allows us to control the trade-off between utility and privacy or fairness through the Lagrange multiplier parameter, and (iii) can be comfortably incorporated to common representation learning algorithms such as the VAE, the β-VAE, the VIB, or the nonlinear IB.