Privacy-Friendly Mobility Analytics using Aggregate Location Data
This work addresses privacy concerns in mobility analytics for urban planners and researchers, offering a deployable solution that balances utility and privacy, though it is incremental in combining existing aggregation protocols with new modeling techniques.
The paper tackles the problem of conducting mobility analytics while preserving user privacy by using aggregate location data, and demonstrates a novel time series modeling approach that forecasts traffic volumes and detects anomalies with real-world datasets from Transport For London and San Francisco Cabs, achieving deployability through a mobile app prototype with evaluated overheads.
Location data can be extremely useful to study commuting patterns and disruptions, as well as to predict real-time traffic volumes. At the same time, however, the fine-grained collection of user locations raises serious privacy concerns, as this can reveal sensitive information about the users, such as, life style, political and religious inclinations, or even identities. In this paper, we study the feasibility of crowd-sourced mobility analytics over aggregate location information: users periodically report their location, using a privacy-preserving aggregation protocol, so that the server can only recover aggregates -- i.e., how many, but not which, users are in a region at a given time. We experiment with real-world mobility datasets obtained from the Transport For London authority and the San Francisco Cabs network, and present a novel methodology based on time series modeling that is geared to forecast traffic volumes in regions of interest and to detect mobility anomalies in them. In the presence of anomalies, we also make enhanced traffic volume predictions by feeding our model with additional information from correlated regions. Finally, we present and evaluate a mobile app prototype, called Mobility Data Donors (MDD), in terms of computation, communication, and energy overhead, demonstrating the real-world deployability of our techniques.