More Value, Less Privacy, How to Evaluate the Privacy Based on Trajectories Visualization & Analyzation

The trade-off among individual privacy, data utility and data feature of service has been a great concern when designing and evaluating privacy preserving schemes in trajectories publishing. The trajectories data is spatial and temporal correlated strongly. So, Privacy-preserving over them should take the human behaviors and their status into account. In this paper, we develop a novel method to investigate and analyze users' behaviors as well as the crowd density after abstracting users' ROIs. Finally, we evaluate the privacy stress via a well-designed indictor based on the trajectories visualization and analyzation. Experiments show that the method is capable of effectively finding both crowd living patterns and distribution, and the proposed indictor can quantize the mobility data utility precisely in grid.

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