# Poisoning Attacks to Local Differential Privacy Protocols for Trajectory Data

arXiv: https://arxiv.org/abs/2503.07483 · HTML: https://mlsanity.com/paper/2503.07483/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 41 references that are in the map.
- First posted on arXiv 6 Mar 2025.

## Builds on (most cited first, 12 of 12)

- [Manipulation Attacks in Local Differential Privacy](https://mlsanity.com/paper/1909.09630/) (2019)
- [Data Poisoning Attacks to Local Differential Privacy Protocols](https://mlsanity.com/paper/1911.02046/) (2019)
- [LDPTrace: Locally Differentially Private Trajectory Synthesis](https://mlsanity.com/paper/2302.06180/) (2023)
- [Poisoning Attacks to Local Differential Privacy Protocols for Key-Value Data](https://mlsanity.com/paper/2111.11534/) (2021)
- [Fine-grained Poisoning Attack to Local Differential Privacy Protocols for Mean and Variance Estimation](https://mlsanity.com/paper/2205.11782/) (2022)
- [Real-World Trajectory Sharing with Local Differential Privacy](https://mlsanity.com/paper/2108.02084/) (2021)
- [Trajectory Data Collection with Local Differential Privacy](https://mlsanity.com/paper/2307.09339/) (2023)
- [On the Robustness of LDP Protocols for Numerical Attributes under Data Poisoning Attacks](https://mlsanity.com/paper/2403.19510/) (2024)
- [Real-Time Trajectory Synthesis with Local Differential Privacy](https://mlsanity.com/paper/2404.11450/) (2024)
- [Data Poisoning Attacks to Locally Differentially Private Frequent Itemset Mining Protocols](https://mlsanity.com/paper/2406.19466/) (2024)
- [Frequency-based Randomization for Guaranteeing Differential Privacy in Spatial Trajectories](https://mlsanity.com/paper/2207.03722/) (2022)
- [Tourist route optimization in the context of Covid-19 pandemic](https://mlsanity.com/paper/2104.07663/) (2021)

## Built on by (most cited first, 1 of 1)

- [Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols](https://mlsanity.com/paper/2506.09803/) (2025)

## Abstract (arXiv)

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 textscTraP, a heuristic algorithm for data underlinePoisoning attacks using a prefix-suffix method to optimize fake underlineTrajectory 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.