We propose a novel algorithm to ensure @math -differential privacy for answering range queries on trajectory data. In order to guarantee privacy, differential privacy mechanisms add noise to either data or query, thus introducing errors to queries made and potentially decreasing the utility of information. In contrast to the state-of-the-art, our method achieves significantly lower error as it is the first data- and query-aware approach for such queries. The key challenge for answering range queries on trajectory data privately is to ensure an accurate count. Simply representing a trajectory as a set instead of of points will generally lead to highly inaccurate query answers as it ignores the sequential dependency of location points in trajectories, i.e., will violate the consistency of trajectory data. Furthermore, trajectories are generally unevenly distributed across a city and adding noise uniformly will generally lead to a poor utility. To achieve differential privacy, our algorithm adaptively adds noise to the input data according to the given query set. It first privately partitions the data space into uniform regions and computes the traffic density of each region. The regions and their densities, in addition to the given query set, are then used to estimate the distribution of trajectories over the queried space, which ensures high accuracy for the given query set. We show the accuracy and efficiency of our algorithm using extensive empirical evaluations on real and synthetic data sets.
We propose a novel approach to privacy-preserving analytical processing within a distributed setting, and tackle the problem of obtaining aggregated information about vehicle traffic in a city from movement data collected by individual vehicles and shipped to a central server. Movement data are sensitive because people’s whereabouts have the potential to reveal intimate personal traits, such as religious or sexual preferences, and may allow re-identification of individuals in a database. We provide a privacy-preserving framework for movement data aggregation based on trajectory generalization in a distributed environment. The proposed solution, based on the differential privacy model and on sketching techniques for efficient data compression, provides a formal data protection safeguard. Using real-life data, we demonstrate the effectiveness of our approach also in terms of data utility preserved by the data transformation.