Digital Privacy Under Attack: Challenges and Enablers

TL;DR

This paper provides a comprehensive analysis of privacy attacks and countermeasures in data-driven systems, categorizing attack types, evaluating defenses, and discussing challenges and future research directions.

Contribution

It systematically classifies privacy attacks and assesses countermeasures, highlighting the strengths and limitations of differential privacy and other techniques.

Findings

Differential privacy offers strong theoretical guarantees but faces practical challenges.

Countermeasures like perturbation and randomization have privacy-utility tradeoffs.

Emerging attacks can exploit vulnerabilities in existing privacy-preserving methods.

Abstract

We present a comprehensive analysis of privacy attacks and countermeasures in data-driven systems. We systematically categorize attacks targeting three domains: anonymous data (linkage and structural attacks), statistical aggregates (reconstruction and differential attacks), and privacy-preserving models (extraction, reconstruction, membership inference, and inversion attacks). For each category, we analyze attack methodologies, adversary capabilities, and vulnerability mechanisms. We further evaluate countermeasures including perturbation techniques, randomization methods, query auditing, and model-level defenses, examining their effectiveness and inherent privacy-utility tradeoffs. Our analysis reveals that while differential privacy offers strong theoretical guarantees, it faces implementation challenges and potential vulnerabilities to emerging attacks. We identify critical research directions and provide researchers and practitioners with a structured framework for understanding privacy resilience in increasingly complex data ecosystems.