Poisoning Attacks to Local Differential Privacy Protocols for Trajectory Data

• URL: http://arxiv.org/abs/2503.07483v1
• Date: Thu, 06 Mar 2025 02:31:45 GMT
• Title: Poisoning Attacks to Local Differential Privacy Protocols for Trajectory Data
• Authors: I-Jung Hsu, Chih-Hsun Lin, Chia-Mu Yu, Sy-Yen Kuo, Chun-Ying Huang,
• Abstract summary: Trajectory data, which tracks movements through geographic locations, is crucial for improving real-world applications.<n>Local differential privacy (LDP) offers a solution by allowing individuals to locally perturb their trajectory data before sharing it.<n>Despite its privacy benefits, LDP protocols are vulnerable to data poisoning attacks, where attackers inject fake data to manipulate aggregated results.
• Score: 14.934626547047763
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Trajectory data, which tracks movements through geographic locations, is crucial for improving real-world applications. However, collecting such sensitive data raises considerable privacy concerns. Local differential privacy (LDP) offers a solution by allowing individuals to locally perturb their trajectory data before sharing it. Despite its privacy benefits, LDP protocols are vulnerable to data poisoning attacks, where attackers inject fake data to manipulate aggregated results. In this work, we make the first attempt to analyze vulnerabilities in several representative LDP trajectory protocols. We propose \textsc{TraP}, a heuristic algorithm for data \underline{P}oisoning attacks using a prefix-suffix method to optimize fake \underline{Tra}jectory selection, significantly reducing computational complexity. Our experimental results demonstrate that our attack can substantially increase target pattern occurrences in the perturbed trajectory dataset with few fake users. This study underscores the urgent need for robust defenses and better protocol designs to safeguard LDP trajectory data against malicious manipulation.

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