In Search of Lost Utility: Private Location Data

TL;DR

This paper introduces a novel privacy-preserving method for generating high-fidelity synthetic location datasets that retain utility and realism by modeling vehicular mobility patterns with deep generative models and Markov Chain Monte Carlo sampling.

Contribution

It presents a new technique combining variational autoencoders, transition modeling, and MCMC to privately generate realistic high-dimensional location data with detailed time information.

Findings

Synthetic data is highly realistic and scalable.

The method outperforms two state-of-the-art approaches.

Generated datasets maintain privacy while preserving utility.

Abstract

The unavailability of training data is a permanent source of much frustration in research, especially when it is due to privacy concerns. This is particularly true for location data since previous techniques all suffer from the inherent sparseness and high dimensionality of location trajectories which render most techniques impractical, resulting in unrealistic traces and unscalable methods. Moreover, time information of location visits is usually dropped, or its resolution is drastically reduced. In this paper we present a novel technique for privately releasing a composite generative model and whole high-dimensional location datasets with detailed time information. To generate high-fidelity synthetic data, we leverage several peculiarities of vehicular mobility such as its language-like characteristics ("you should know a location by the company it keeps") or how humans plan their trips from one point to the other. We model the generator distribution of the dataset by first constructing a variational autoencoder to generate the source and destination locations, and the corresponding timing of trajectories. Next, we compute transition probabilities between locations with a feed forward network, and build a transition graph from the output of this model, which approximates the distribution of all paths between the source and destination (at a given time). Finally, a path is sampled from this distribution with a Markov Chain Monte Carlo method. The generated synthetic dataset is highly realistic, scalable, provides good utility and, nonetheless, provably private. We evaluate our model against two state-of-the-art methods and three real-life datasets demonstrating the benefits of our approach.