  Directly releasing those data raises privacy and liability (e.g., due to
unauthorized distribution of such datasets) concerns since location data
contain users' sensitive information, e.g., regular moving patterns and
favorite spots. To address this, we propose a novel fingerprinting scheme that
simultaneously identifies unauthorized redistribution of location datasets and
provides differential privacy guarantees for the shared data. Observing data
utility degradation due to differentially-private mechanisms, we introduce a
utility-focused post-processing scheme to regain spatio-temporal correlations
between points in a location trajectory. We further integrate this
post-processing scheme into our fingerprinting scheme as a sampling method. The
proposed fingerprinting scheme alleviates the degradation in the utility of the
shared dataset due to the noise introduced by differentially-private mechanisms
(i.e., adds the fingerprint by preserving the publicly known statistics of the
data). Meanwhile, it does not violate differential privacy throughout the
entire process due to immunity to post-processing, a fundamental property of
differential privacy. Our proposed fingerprinting scheme is robust against
known and well-studied attacks against a fingerprinting scheme including random
flipping attacks, correlation-based flipping attacks, and collusions among
multiple parties, which makes it hard for the attackers to infer the
fingerprint codes and avoid accusation. Via experiments on two real-life
location datasets and two synthetic ones, we show that our scheme achieves high
fingerprinting robustness and outperforms existing approaches. Besides, the
proposed fingerprinting scheme increases data utility for
differentially-private datasets, which is beneficial for data analyzers.
