# Post-processing of Differentially Private Data: A Fairness Perspective

arXiv: https://arxiv.org/abs/2201.09425 · HTML: https://mlsanity.com/paper/2201.09425/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 24 Jan 2022.

## Builds on (most cited first, 6 of 6)

- [Differentially Private Fair Learning](https://mlsanity.com/paper/1812.02696/) (2018)
- [Fair Decision Making using Privacy-Protected Data](https://mlsanity.com/paper/1905.12744/) (2019)
- [Decision Making with Differential Privacy under a Fairness Lens](https://mlsanity.com/paper/2105.07513/) (2021)
- [Bias and Variance of Post-processing in Differential Privacy](https://mlsanity.com/paper/2010.04327/) (2020)
- [An Economic Analysis of Privacy Protection and Statistical Accuracy as Social Choices](https://mlsanity.com/paper/1808.06303/) (2018)
- [Differential Privacy for Power Grid Obfuscation](https://mlsanity.com/paper/1901.06949/) (2019)

## Built on by (most cited first, 9 of 9)

- [Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey](https://mlsanity.com/paper/2202.08187/) (2022)
- [A Bias-Accuracy-Privacy Trilemma for Statistical Estimation](https://mlsanity.com/paper/2301.13334/) (2023)
- [Node Injection Link Stealing Attack](https://mlsanity.com/paper/2307.13548/) (2023)
- [On The Fairness Impacts of Hardware Selection in Machine Learning](https://mlsanity.com/paper/2312.03886/) (2023)
- [Fairness Issues and Mitigations in (Differentially Private) Socio-Demographic Data Processes](https://mlsanity.com/paper/2408.08471/) (2024)
- [Privacy at a Price: Exploring its Dual Impact on AI Fairness](https://mlsanity.com/paper/2404.09391/) (2024)
- [Differentially Private Data Release on Graphs: Inefficiencies and Unfairness](https://mlsanity.com/paper/2408.05246/) (2024)
- [Differential Privacy in Machine Learning: A Survey from Symbolic AI to LLMs](https://mlsanity.com/paper/2506.11687/) (2025)
- [Differentially Private Natural Gradient Descent](https://mlsanity.com/paper/2607.05866/) (2026)

## Abstract (arXiv)

Post-processing immunity is a fundamental property of differential privacy: it enables arbitrary data-independent transformations to differentially private outputs without affecting their privacy guarantees. Post-processing is routinely applied in data-release applications, including census data, which are then used to make allocations with substantial societal impacts. This paper shows that post-processing causes disparate impacts on individuals or groups and analyzes two critical settings: the release of differentially private datasets and the use of such private datasets for downstream decisions, such as the allocation of funds informed by US Census data. In the first setting, the paper proposes tight bounds on the unfairness of traditional post-processing mechanisms, giving a unique tool to decision-makers to quantify the disparate impacts introduced by their release. In the second setting, this paper proposes a novel post-processing mechanism that is (approximately) optimal under different fairness metrics, either reducing fairness issues substantially or reducing the cost of privacy. The theoretical analysis is complemented with numerical simulations on Census data.