Differential Private Trajectory Protection of Moving Objects

Location privacy and security of spatio-temporal data has come under high scrutiny in the past years. This has rekindled enormous research interest. So far, most of the research studies that attempt to address location privacy are based on the k-Anonymity privacy paradigm. In this paper, we propose a novel technique to ensure location privacy in stream and non-stream mobility data using differential privacy. We portray incoming stream or non-stream mobility data emanating from GPS-enabled devices as a differential privacy problem and rigorously define a spatio-temporal sensitivity function for a trajectory metric space. Privacy is achieved through path perturbation in both the space and time domain. In addition, we introduce a new notion of Nearest Neighbor Anchor Resource to add more contextual meaning in the face of uncertainty to the perturbed trajectory path. Unlike k-Anonymity techniques that require more mobile objects to achieve strong anonymity; we show that our approach provides stronger privacy even for a single moving mobile object, outliers or mobile objects in sparsely populated regions.