  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 R\'enyi 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.
