# Fairness Degrading Adversarial Attacks Against Clustering Algorithms

arXiv: https://arxiv.org/abs/2110.12020 · HTML: https://mlsanity.com/paper/2110.12020/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 38 references that are in the map.
- First posted on arXiv 22 Oct 2021.

## Builds on (most cited first, 20 of 20)

- [Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples](https://mlsanity.com/paper/1605.07277/) (2016)
- [Fairness in Machine Learning: A Survey](https://mlsanity.com/paper/2010.04053/) (2020)
- [Fair Clustering Through Fairlets](https://mlsanity.com/paper/1802.05733/) (2018)
- [Fair Algorithms for Clustering](https://mlsanity.com/paper/1901.02393/) (2019)
- [Scalable Fair Clustering](https://mlsanity.com/paper/1902.03519/) (2019)
- [Guarantees for Spectral Clustering with Fairness Constraints](https://mlsanity.com/paper/1901.08668/) (2019)
- [Proportionally Fair Clustering](https://mlsanity.com/paper/1905.03674/) (2019)
- [A cost function for similarity-based hierarchical clustering](https://mlsanity.com/paper/1510.05043/) (2015)
- [Poisoning Attacks on Algorithmic Fairness](https://mlsanity.com/paper/2004.07401/) (2020)
- [Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning](https://mlsanity.com/paper/2006.12621/) (2020)
- [Exacerbating Algorithmic Bias through Fairness Attacks](https://mlsanity.com/paper/2012.08723/) (2020)
- [On the cost of essentially fair clusterings](https://mlsanity.com/paper/1811.10319/) (2018)
- [On Adversarial Bias and the Robustness of Fair Machine Learning](https://mlsanity.com/paper/2006.08669/) (2020)
- [A Center in Your Neighborhood: Fairness in Facility Location](https://mlsanity.com/paper/1908.09041/) (2019)
- [Fairness in Clustering with Multiple Sensitive Attributes](https://mlsanity.com/paper/1910.05113/) (2019)
- [Is Data Clustering in Adversarial Settings Secure?](https://mlsanity.com/paper/1811.09982/) (2018)
- [A black-box adversarial attack for poisoning clustering](https://mlsanity.com/paper/2009.05474/) (2020)
- [Poisoning Behavioral Malware Clustering](https://mlsanity.com/paper/1811.09985/) (2018)
- [Suspicion-Free Adversarial Attacks on Clustering Algorithms](https://mlsanity.com/paper/1911.07015/) (2019)
- [Variational Fair Clustering](https://mlsanity.com/paper/1906.08207/) (2019)

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

- [Robust Fair Clustering: A Novel Fairness Attack and Defense Framework](https://mlsanity.com/paper/2210.01953/) (2022)
- [Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networks](https://mlsanity.com/paper/2209.05957/) (2022)
- [Deceptive Fairness Attacks on Graphs via Meta Learning](https://mlsanity.com/paper/2310.15653/) (2023)
- [Measuring Equality in Machine Learning Security Defenses: A Case Study in Speech Recognition](https://mlsanity.com/paper/2302.08973/) (2023)
- [Geometric Algorithms for k-NN Poisoning](https://mlsanity.com/paper/2306.12377/) (2023)

## Abstract (arXiv)

Clustering algorithms are ubiquitous in modern data science pipelines, and are utilized in numerous fields ranging from biology to facility location. Due to their widespread use, especially in societal resource allocation problems, recent research has aimed at making clustering algorithms fair, with great success. Furthermore, it has also been shown that clustering algorithms, much like other machine learning algorithms, are susceptible to adversarial attacks where a malicious entity seeks to subvert the performance of the learning algorithm. However, despite these known vulnerabilities, there has been no research undertaken that investigates fairness degrading adversarial attacks for clustering. We seek to bridge this gap by formulating a generalized attack optimization problem aimed at worsening the group-level fairness of centroid-based clustering algorithms. As a first step, we propose a fairness degrading attack algorithm for k-median clustering that operates under a whitebox threat model -- where the clustering algorithm, fairness notion, and the input dataset are known to the adversary. We provide empirical results as well as theoretical analysis for our simple attack algorithm, and find that the addition of the generated adversarial samples can lead to significantly lower fairness values. In this manner, we aim to motivate fairness degrading adversarial attacks as a direction for future research in fair clustering.