Fair Play for Individuals, Foul Play for Groups? Auditing Anonymization's Impact on ML Fairness

Machine learning (ML) algorithms are heavily based on the availability of training data, which, depending on the domain, often includes sensitive information about data providers. This raises critical privacy concerns. Anonymization techniques have emerged as a practical solution to address these issues by generalizing features or suppressing data to make it more difficult to accurately identify individuals. Although recent studies have shown that privacy-enhancing technologies can influence ML predictions across different subgroups, thus affecting fair decision-making, the specific effects of anonymization techniques, such as $k$-anonymity, $\ell$-diversity, and $t$-closeness, on ML fairness remain largely unexplored. In this work, we systematically audit the impact of anonymization techniques on ML fairness, evaluating both individual and group fairness. Our quantitative study reveals that anonymization can degrade group fairness metrics by up to four orders of magnitude. Conversely, similarity-based individual fairness metrics tend to improve under stronger anonymization, largely as a result of increased input homogeneity. By analyzing varying levels of anonymization across diverse privacy settings and data distributions, this study provides critical insights into the trade-offs between privacy, fairness, and utility, offering actionable guidelines for responsible AI development. Our code is publicly available at: https://github.com/hharcolezi/anonymity-impact-fairness.

Indoor Location Fingerprinting Privacy: A Comprehensive Survey

The pervasive integration of Indoor Positioning Systems (IPS) arises from the limitations of Global Navigation Satellite Systems (GNSS) in indoor environments, leading to the widespread adoption of Location-Based Services (LBS). Specifically, indoor location fingerprinting employs diverse signal fingerprints from user devices, enabling precise location identification by Location Service Providers (LSP). Despite its broad applications across various domains, indoor location fingerprinting introduces a notable privacy risk, as both LSP and potential adversaries inherently have access to this sensitive information, compromising users' privacy. Consequently, concerns regarding privacy vulnerabilities in this context necessitate a focused exploration of privacy-preserving mechanisms. In response to these concerns, this survey presents a comprehensive review of Privacy-Preserving Mechanisms in Indoor Location Fingerprinting (ILFPPM) based on cryptographic, anonymization, differential privacy (DP), and federated learning (FL) techniques. We also propose a distinctive and novel grouping of privacy vulnerabilities, adversary and attack models, and available evaluation metrics specific to indoor location fingerprinting systems. Given the identified limitations and research gaps in this survey, we highlight numerous prospective opportunities for future investigation, aiming to motivate researchers interested in advancing this field. This survey serves as a valuable reference for researchers and provides a clear overview for those beyond this specific research domain.

Differentially Private GANs for Generating Synthetic Indoor Location Data

The advent of location-based services has led to the widespread adoption of indoor localization systems, which enable location tracking of individuals within enclosed spaces such as buildings. While these systems provide numerous benefits such as improved security and personalized services, they also raise concerns regarding privacy violations. As such, there is a growing need for privacy-preserving solutions that can protect users' sensitive location information while still enabling the functionality of indoor localization systems. In recent years, Differentially Private Generative Adversarial Networks (DPGANs) have emerged as a powerful methodology that aims to protect the privacy of individual data points while generating realistic synthetic data similar to original data. DPGANs combine the power of generative adversarial networks (GANs) with the privacy-preserving technique of differential privacy (DP). In this paper, we introduce an indoor localization framework employing DPGANs in order to generate privacy-preserving indoor location data. We evaluate the performance of our framework on a real-world indoor localization dataset and demonstrate its effectiveness in preserving privacy while maintaining the accuracy of the localization system.