Trajectory Data Collection with Local Differential Privacy

by   Yuemin Zhang, et al.

Trajectory data collection is a common task with many applications in our daily lives. Analyzing trajectory data enables service providers to enhance their services, which ultimately benefits users. However, directly collecting trajectory data may give rise to privacy-related issues that cannot be ignored. Local differential privacy (LDP), as the de facto privacy protection standard in a decentralized setting, enables users to perturb their trajectories locally and provides a provable privacy guarantee. Existing approaches to private trajectory data collection in a local setting typically use relaxed versions of LDP, which cannot provide a strict privacy guarantee, or require some external knowledge that is impractical to obtain and update in a timely manner. To tackle these problems, we propose a novel trajectory perturbation mechanism that relies solely on an underlying location set and satisfies pure ϵ-LDP to provide a stringent privacy guarantee. In the proposed mechanism, each point's adjacent direction information in the trajectory is used in its perturbation process. Such information serves as an effective clue to connect neighboring points and can be used to restrict the possible region of a perturbed point in order to enhance utility. To the best of our knowledge, our study is the first to use direction information for trajectory perturbation under LDP. Furthermore, based on this mechanism, we present an anchor-based method that adaptively restricts the region of each perturbed trajectory, thereby significantly boosting performance without violating the privacy constraint. Extensive experiments on both real-world and synthetic datasets demonstrate the effectiveness of the proposed mechanisms.


page 1

page 2

page 3

page 4


Frequency-based Randomization for Guaranteeing Differential Privacy in Spatial Trajectories

With the popularity of GPS-enabled devices, a huge amount of trajectory ...

Real-World Trajectory Sharing with Local Differential Privacy

Sharing trajectories is beneficial for many real-world applications, suc...

Zip to Zip-it: Compression to Achieve Local Differential Privacy

Local differential privacy techniques for numerical data typically trans...

LDPTrace: Locally Differentially Private Trajectory Synthesis

Trajectory data has the potential to greatly benefit a wide-range of rea...

Lclean: A Plausible Approach to Individual Trajectory Data Sanitization

In recent years, with the continuous development of significant data ind...

Interval Privacy: A Framework for Data Collection

The emerging public awareness and government regulations of data privacy...

Spatio-temporal Trajectory Dataset Privacy Based on Network Traffic Control

Collection of user's location and trajectory information that contains r...

Please sign up or login with your details

Forgot password? Click here to reset