In the era of Big Data, the development of artificial intelligence (AI) systems presents both opportunities and challenges, particularly concerning privacy and fairness. While differential privacy (DP) has emerged as a robust methodology for preserving privacy in real-world applications, its local variant (LDP) specifically addresses trust issues by removing the reliance on a centralized server. Equally critical, conducting fairness audits of AI systems helps identify and mitigate discriminatory outcomes in machine learning. Although the relationship between DP and fairness is inherently multifaceted, this paper offers a detailed empirical examination of how collecting multi-dimensional sensitive attributes under LDP affects fairness in binary classification tasks. Our findings reveal that LDP can slightly improve fairness without substantially degrading model performance—challenging the notion that DP necessarily exacerbates unfairness. We demonstrate these results by evaluating seven state-of-the-art LDP protocols on three benchmark datasets, using established group fairness metrics. Moreover, we propose a novel privacy budget allocation scheme that incorporates varying domain sizes of sensitive attributes, achieving a superior privacy–utility–fairness trade-off compared to existing solutions.

Machine learning (ML) algorithms rely primarily on the availability of training data, and, depending on the domain, these data may include sensitive information about the data providers, thus leading to significant privacy issues. Differential privacy (DP) is the predominant solution for privacy-preserving ML, and the local model of DP is the preferred choice when the server or the data collector are not trusted. Recent experimental studies have shown that local DP can impact ML prediction for different subgroups of individuals, thus affecting fair decision-making. However, the results are conflicting in the sense that some studies show a positive impact of privacy on fairness while others show a negative one. In this work, we conduct a systematic and formal study of the effect of local DP on fairness. Specifically, we perform a quantitative study of how the fairness of the decisions made by the ML model changes under local DP for different levels of privacy and data distributions. In particular, we provide bounds in terms of the joint distributions and the privacy level, delimiting the extent to which local DP can impact the fairness of the model. We characterize the cases in which privacy reduces discrimination and those with the opposite effect. We validate our theoretical findings on synthetic and real-world datasets. Our results are preliminary in the sense that, for now, we study only the case of one sensitive attribute, and only statistical disparity, conditional statistical disparity, and equal opportunity difference.

Abstract Traffic analysis is the process of extracting useful/sensitive information from observed network traffic. Typical use cases include malware detection and website fingerprinting attacks. High accuracy traffic analysis techniques use machine learning algorithms (e.g. SVM, kNN) and require to split the traffic into correctly separated blocks. Inspired by digital forensics techniques, we propose a new network traffic analysis approach based on similarity digest. The approach features several advantages compared to existing techniques, namely, fast signature generation, compact signature representation using Bloom filters, efficient similarity detection between packet traces of arbitrary sizes, and in particular dropping the traffic splitting requirement altogether. Experimental results show very promising results on VPN and malware traffic, but low results on Tor traffic due mainly to the single-size cells feature.