Trading Optimality for Performance in Location Privacy

TL;DR

This paper introduces a scalable approach to location privacy in LBSs by reducing the complexity of linear optimization constraints, balancing privacy, utility, and computational efficiency.

Contribution

It proposes a method to decrease the number of constraints from O(n^3) to O(n^2), making optimal privacy-utility trade-offs feasible for larger location sets.

Findings

Significant performance improvement in constraint handling.

Acceptable utility loss in practical scenarios.

Maintains a good privacy-utility balance despite reduced optimality.

Abstract

Location-Based Services (LBSs) provide invaluable aid in the everyday activities of many individuals, however they also pose serious threats to the user' privacy. There is, therefore, a growing interest in the development of mechanisms to protect location privacy during the use of LBSs. Nowadays, the most popular methods are probabilistic, and the so-called optimal method achieves an optimal trade-off between privacy and utility by using linear optimization techniques. Unfortunately, due to the complexity of linear programming, the method is unfeasible for a large number n of locations, because the constraints are $O(n^3)$. In this paper, we propose a technique to reduce the number of constraints to $O(n^2)$, at the price of renouncing to perfect optimality. We show however that on practical situations the utility loss is quite acceptable, while the gain in performance is significant.