Protecting Location Privacy Through Path Confusion

We present a path perturbation algorithm which can maximize users’ location privacy given a quality of service constraint. This work concentrates on a class of applications that continuously collect location samples from a large group of users, where just removing user identifiers from all samples is insufficient because an adversary could use trajectory information to track paths and follow users’ footsteps home. The key idea underlying the perturbation algorithm is to cross paths in areas where at least two users meet. This increases the chances that an adversary would confuse the paths of different users. We first formulate this privacy problem as a constrained optimization problem and then develop heuristics for an efficient privacy algorithm. Using simulations with randomized movement models we verify that the algorithm improves privacy while minimizing the perturbation of location samples.