Resisting TUL attack: balancing data privacy and utility on trajectory via collaborative adversarial learning

Nowadays, large-scale individual trajectories can be collected by various location-based social network services, which enables us to better understand human mobility patterns. However, the trajectory data usually contain sensitive information of users, raising considerable concerns about the privacy issue. Existing methods for protecting user trajectory data face two major challenges. First, existing methods generally emphasize on data privacy but largely ignore the data utility. Second, most existing work focus on protecting the privacy of users’ check-in locations, which is not sufficient to protect against the trajectory-user linking (TUL) attack that infers a user’s identity based on her/his trajectories. In this paper, we for the first time propose a collaborative adversarial learning model named BPUCAL to effectively resist the TUL attack and preserve the data utility simultaneously. The general idea is to fool the TUL model by adding a small perturbation on the original trajectory data to balance the data utility and privacy. BPUCAL perturbs a few numbers of carefully identified check-ins of a trajectory which are pivotal for a TUL model to infer the identity of a user. Specifically, BPUCAL contains three parts: a perturbation generator, a discriminator, and a TUL model. The generator aims to produce learnable noise and adds it to the original trajectories for obtaining perturbed trajectories. The perturbed trajectories with a minimal changes compared to the original trajectories can deceive both the discriminator and the TUL model. Extensive experiments are conducted over two real-world datasets. The results show the superior performance of our proposal in balancing data privacy and utility on trajectory data by comparison with baselines.