Mobile users participate in numerous social media applications that revolve around user locations, and receive customized services and recommendations tailored to their whereabouts. Large amounts of trajectory data become available as a byproduct of such services. Studying such data reveals travel patterns which can benefit transportation planning, public safety, etc. However, disclosing such data may lead to serious breaches of privacy. We propose a privacy-preserving approach to mining representative travel patterns using differential privacy (DP). Our solution consists of a sampling algorithm based on the exponential mechanism (EM) of DP which uses public road network information to increase sanitization accuracy. Extensive experimental results on realistic workloads show that the proposed protection technique preserves data precision and is computationally efficient.
['Mihai Maruseac', 'Gabriel Ghinita']
Differentially-Private Mining of Representative Travel Patterns