Location Trace Privacy Under Conditional Priors
This work addresses privacy concerns for users of location-based services, offering a novel approach to handle conditionally dependent data, though it is incremental in building on existing privacy frameworks.
The paper tackles the challenge of preserving privacy in location-based services when multiple dependent locations are revealed, proposing a Rényi divergence framework to bound expected privacy loss and demonstrating an algorithm under Gaussian process priors that preserves privacy within a fixed radius for sensitive locations.
Providing meaningful privacy to users of location based services is particularly challenging when multiple locations are revealed in a short period of time. This is primarily due to the tremendous degree of dependence that can be anticipated between points. We propose a Rényi divergence based privacy framework for bounding expected privacy loss for conditionally dependent data. Additionally, we demonstrate an algorithm for achieving this privacy under Gaussian process conditional priors. This framework both exemplifies why conditionally dependent data is so challenging to protect and offers a strategy for preserving privacy to within a fixed radius for sensitive locations in a user's trace.