# On the Alignment of Group Fairness with Attribute Privacy

arXiv: https://arxiv.org/abs/2211.10209 · HTML: https://mlsanity.com/paper/2211.10209/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 54 references that are in the map.
- First posted on arXiv 18 Nov 2022.

## Builds on (most cited first, 23 of 23)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Mitigating Unwanted Biases with Adversarial Learning](https://mlsanity.com/paper/1801.07593/) (2018)
- [A Reductions Approach to Fair Classification](https://mlsanity.com/paper/1803.02453/) (2018)
- [Learning Adversarially Fair and Transferable Representations](https://mlsanity.com/paper/1802.06309/) (2018)
- [Differential Privacy Has Disparate Impact on Model Accuracy](https://mlsanity.com/paper/1905.12101/) (2019)
- [Censoring Representations with an Adversary](https://mlsanity.com/paper/1511.05897/) (2015)
- [Controllable Invariance through Adversarial Feature Learning](https://mlsanity.com/paper/1705.11122/) (2017)
- [On the Privacy Risks of Algorithmic Fairness](https://mlsanity.com/paper/2011.03731/) (2020)
- [Overlearning Reveals Sensitive Attributes](https://mlsanity.com/paper/1905.11742/) (2019)
- [AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning](https://mlsanity.com/paper/1805.04810/) (2018)
- [Learning to Pivot with Adversarial Networks](https://mlsanity.com/paper/1611.01046/) (2016)
- [Are Attribute Inference Attacks Just Imputation?](https://mlsanity.com/paper/2209.01292/) (2022)
- [Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey](https://mlsanity.com/paper/2202.08187/) (2022)
- [Empirical observation of negligible fairness-accuracy trade-offs in machine learning for public policy](https://mlsanity.com/paper/2012.02972/) (2020)
- [An Overview of Privacy in Machine Learning](https://mlsanity.com/paper/2005.08679/) (2020)
- [You are Who You Know and How You Behave: Attribute Inference Attacks via Users' Social Friends and Behaviors](https://mlsanity.com/paper/1606.05893/) (2016)
- [On the (In)Feasibility of Attribute Inference Attacks on Machine Learning Models](https://mlsanity.com/paper/2103.07101/) (2021)
- [Understanding Why Generalized Reweighting Does Not Improve Over ERM](https://mlsanity.com/paper/2201.12293/) (2022)
- [Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputs](https://mlsanity.com/paper/2105.12049/) (2021)
- [Inferring Sensitive Attributes from Model Explanations](https://mlsanity.com/paper/2208.09967/) (2022)
- [A Fairness Analysis on Private Aggregation of Teacher Ensembles](https://mlsanity.com/paper/2109.08630/) (2021)
- [Exploiting Fairness to Enhance Sensitive Attributes Reconstruction](https://mlsanity.com/paper/2209.01215/) (2022)
- [Does deep learning model calibration improve performance in class-imbalanced medical image classification?](https://mlsanity.com/paper/2110.00918/) (2021)

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

- [Trustworthy Machine Learning via Memorization and the Granular Long-Tail: A Survey on Interactions, Tradeoffs, and Beyond](https://mlsanity.com/paper/2503.07501/) (2025)
- [Holistic Survey of Privacy and Fairness in Machine Learning](https://mlsanity.com/paper/2307.15838/) (2023)
- [Combining Machine Learning Defenses without Conflicts](https://mlsanity.com/paper/2411.09776/) (2024)
- [Do Fairness Interventions Come at the Cost of Privacy: Evaluations for Binary Classifiers](https://mlsanity.com/paper/2503.06150/) (2025)
- [Amulet: a Python Library for Assessing Interactions Among ML Defenses and Risks](https://mlsanity.com/paper/2509.12386/) (2025)
- [Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data](https://mlsanity.com/paper/2607.07471/) (2026)
- [Beyond Frequency: The Role of Redundancy in Large Language Model Memorization](https://mlsanity.com/paper/2506.12321/) (2025)
- [Taipan: A Query-free Transfer-based Multiple Sensitive Attribute Inference Attack Solely from Auxiliary Graphs](https://mlsanity.com/paper/2602.06700/) (2026)
- [SoK: Colluding Adversaries in Machine Learning Pipelines](https://mlsanity.com/paper/2606.10091/) (2026)

## Abstract (arXiv)

Group fairness and privacy are fundamental aspects in designing trustworthy machine learning models. Previous research has highlighted conflicts between group fairness and different privacy notions. We are the first to demonstrate the alignment of group fairness with the specific privacy notion of attribute privacy in a blackbox setting. Attribute privacy, quantified by the resistance to attribute inference attacks (AIAs), requires indistinguishability in the target model's output predictions. Group fairness guarantees this thereby mitigating AIAs and achieving attribute privacy. To demonstrate this, we first introduce AdaptAIA, an enhancement of existing AIAs, tailored for real-world datasets with class imbalances in sensitive attributes. Through theoretical and extensive empirical analyses, we demonstrate the efficacy of two standard group fairness algorithms (i.e., adversarial debiasing and exponentiated gradient descent) against AdaptAIA. Additionally, since using group fairness results in attribute privacy, it acts as a defense against AIAs, which is currently lacking. Overall, we show that group fairness aligns with attribute privacy at no additional cost other than the already existing trade-off with model utility.