GeoRecover: Recovery From Poisoning Attacks for LDP-Enabled Spatial Density Aggregation

Abstract

The spatial density distribution collected and aggregated from users’ trajectory data is vital for location-based services like regional popularity analysis and congestion measurement. However, spatial density aggregation poses privacy concerns since trajectory data usually originate from users. Local differential privacy (LDP) addresses these concerns by allowing users to perturb their data before reporting it. Yet, LDP is vulnerable to poisoning attacks where attackers manipulate data from malicious users. Recent studies attempt to defend against such attacks in LDP-enabled frequency estimation but suffer from inaccurate data recovery due to empirical presets of malicious user proportions and inaccurate malicious data estimation. These issues worsen in spatial density aggregation, as high-dimensional trajectory data help conceal malicious information. In this work, we propose GeoRecover, a method to defend against poisoning attacks in LDP-enabled spatial density aggregation by addressing previous limitations. GeoRecover designs an adaptive model to unify these attacks. Under this model, GeoRecover estimates the proportion of malicious users using statistical differences between genuine and malicious data and learns malicious data statistics through LDP properties. This allows GeoRecover to recover accurate spatial density distribution by subtracting malicious users’ contributions. Evaluations on two real-world datasets show GeoRecover outperforms state-of-the-art methods in recovery accuracy, defense capability, and practical performance.