Location Trace Privacy Under Conditional Priors
This work addresses privacy concerns for users of location-based services, offering a novel approach for conditionally dependent data, though it is incremental in building on differential privacy.
The paper tackles the challenge of protecting location privacy in traces with dependent data points by proposing a Rényi differentially private framework to bound expected privacy loss, achieving privacy within a fixed radius for every user location.
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 differentially private 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 every user location in a trace.