Privacy-Preserving Trajectory Data Publishing by Dynamic Anonymization with Bounded Distortion

Publication of trajectory data that contain rich information of vehicles in the dimensions of time and space (location) enables online monitoring and supervision of vehicles in motion and offline traffic analysis for various management tasks. However, it also provides security holes for privacy breaches as exposing individual’s privacy information to public may results in attacks threatening individual’s safety. Therefore, increased attention has been made recently on the privacy protection of trajectory data publishing. However, existing methods, such as generalization via anonymization and suppression via randomization, achieve protection by modifying the original trajectory to form a publishable trajectory, which results in significant data distortion and hence a low data utility. In this work, we propose a trajectory privacy-preserving method called dynamic anonymization with bounded distortion. In our method, individual trajectories in the original trajectory set are mixed in a localized manner to form synthetic trajectory data set with a bounded distortion for publishing, which can protect the privacy of location information associated with individuals in the trajectory data set and ensure a guaranteed utility of the published data both individually and collectively. Through experiments conducted on real trajectory data of Guangzhou City Taxi statistics, we evaluate the performance of our proposed method and compare it with the existing mainstream methods in terms of privacy preservation against attacks and trajectory data utilization. The results show that our proposed method achieves better performance on data utilization than the existing methods using globally static anonymization, without trading off the data security against attacks.

Protecting Private Inputs: Bounded Distortion Guarantees With Randomised Approximations

Computing a function of some private inputs while maintaining the confidentiality of those inputs is an important problem, to which Differential Privacy and Secure Multi-party Computation can offer solutions under specific assumptions. Research in randomised algorithms aims at improving the privacy of such inputs by randomising the output of a computation while ensuring that large distortions of outputs occur with low probability. But use cases such as e-voting or auctions will not tolerate large distortions at all. Thus, we develop a framework for randomising the output of a privacypreserving computation, while guaranteeing that output distortions stay within a specified bound. We analyse the privacy gains of our approach and characterise them more precisely for our notion of sparse functions. We build randomisation algorithms, running in linearithmic time in the number of possible input values, for this class of functions and we prove that the computed randomisations maximise the inputs’ privacy. Experimental work demonstrates significant privacy gains when compared with existing approaches that guarantee distortion bounds, also for non-sparse functions.

Foundations of quasiconformal analysis of a two-index scale of spatial mappings

We define a scale of mappings that depends on two real parameters p and q, and a weight function θ. In the case of q = p = n, θ ≡ 1, we obtain the well known mappingswith bounded distortion. Mappings of a two-index scale inherit many properties of mappings with bounded distortion. They are used for solving a few problems of global analysis and applied problems.