Differentially-private next-location prediction with neural networks

The emergence of mobile apps (e.g., location-based services, geo-social networks, ride-sharing) led to the collection of vast amounts of trajectory data that greatly benefit the understanding of individual mobility. One problem of particular interest is next-location prediction, which facilitates location-based advertising, point-of-interest recommendation, traffic optimization, etc. However, using individual trajectories to build prediction models introduces serious privacy concerns, since exact whereabouts of users can disclose sensitive information such as their health status or lifestyle choices. Several research efforts focused on privacy-preserving next-location prediction, but they have serious limitations: some use outdated privacy models (e.g., k-anonymity), while others employ learning models with limited expressivity (e.g., matrix factorization). More recent approaches (e.g., DP-SGD) integrate the powerful differential privacy model with neural networks, but they provide only generic and difficult-to-tune methods that do not perform well on location data, which is inherently skewed and sparse. We propose a technique that builds upon DP-SGD, but adapts it for the requirements of next-location prediction. We focus on user-level privacy, a strong privacy guarantee that protects users regardless of how much data they contribute. Central to our approach is the use of the skip-gram model, and its negative sampling technique. Our work is the first to propose differentially-private learning with skip-grams. In addition, we devise data grouping techniques within the skip-gram framework that pool together trajectories from multiple users in order to accelerate learning and improve model accuracy. Experiments conducted on real datasets demonstrate that our approach significantly boosts prediction accuracy compared to existing DP-SGD techniques.