Optimal privacy control for transport network data sharing

In the era of smart cities, Internet of Things, and Mobility-as-a-Service, the need for private operators to willingly share data with public agencies is greater than ever before. However, it is still problematic for private operators to share data with the public due to risks to competitive advantages. A privacy control algorithm is proposed to overcome this key obstacle for private operators sharing complex network-oriented data objects. The algorithm is based on information-theoretic k-anonymity where an operator's tour data is used in conjunction with performance measure accuracy controls to synthesize a set of alternative tours with diffused probabilities for sampling during a query. The algorithm is proven to converge sublinearly toward constrained maximum entropy under certain asymptotic conditions with measurable optimality gap. Computational experiments verify the applicability to multi-vehicle fleet tour data; confirm that reverse engineered parameters from the diffused data results in controllable sampling error; and tests conducted on a set of realistic routing records from travel data in Long Island, NY, demonstrate the use of the methodology from both the adversary and user perspectives.