P4Mobi: A Probabilistic Privacy-Preserving Framework for Publishing Mobility Datasets

Abstract

The large-scale collection of individuals’ mobility data poses serious privacy concerns. Instead of perturbing data by adding noise to the raw location data to preserve privacy of individuals, we propose an approach that achieves privacy-preservation at the statistics level of aggregating mobility datasets with the probabilistic data structure Count-Min Sketch (CMS) [1] , which has been widely used to provide efficient statistic functions with a tunable error bound. We use CMS to estimate the population density distributions in the mobility datasets, where the error bound determines utility guarantees. We develop P4Mobi , a novel P robabilistic P rivacy- P reserving P ublishing framework for Mobi lity datasets that protects individuals’ privacy while complying to a specific utility requirement. We empirically validate the performance of P4Mobi in terms of utility and privacy-preservation by demonstrating its resilience against a recently proposed reconstruction attack model using two real-world datasets. We compare P4Mobi to two state-of-the-art methods and show that with the same level of privacy achieved against our attack model, P4Mobi significantly improves the utility of the published mobility datasets by up to $20\%$ . We also provide a theoretical estimate of the utility achieved by P4Mobi . We found a very consistent match between the estimated and empirical utility of P4Mobi as evaluated on two datasets.