Differentially Private Trajectory Analysis for Points-of-Interest Recommendation

Ubiquitous deployment of low-cost mobile positioning devices and the widespread use of high-speed wireless networks enable massive collection of large-scale trajectory data of individuals moving on road networks. Trajectory data mining finds numerous applications including understanding users' historical travel preferences and recommending places of interest to new visitors. Privacy-preserving trajectory mining is an important and challenging problem as exposure of sensitive location information in the trajectories can directly invade the location privacy of the users associated with the trajectories. In this paper, we propose a differentially private trajectory analysis algorithm for points-of-interest recommendation to users that aims at maximizing the accuracy of the recommendation results while protecting the privacy of the exposed trajectories with differential privacy guarantees. Our algorithm first transforms the raw trajectory dataset into a bipartite graph with nodes representing the users and the points-of-interest and the edges representing the visits made by the users to the locations, and then extracts the association matrix representing the bipartite graph to inject carefully calibrated noise to meet ϵ-differential privacy guarantees. A post-processing of the perturbed association matrix is performed to suppress noise prior to performing a Hyperlink-Induced Topic Search (HITS) on the transformed data that generates an ordered list of recommended points-of-interest. Extensive experiments on a real trajectory dataset show that our algorithm is efficient, scalable and demonstrates high recommendation accuracy while meeting the required differential privacy guarantees

SoK: differentially private publication of trajectory data

Trajectory analysis holds many promises, from improvements in traffic management to routing advice or infrastructure development. However, learning users’ paths is extremely privacy-invasive. Therefore, there is a necessity to protect trajectories such that we preserve the global properties, useful for analysis, while specific and private information of individuals remains inaccessible. Trajectories, however, are difficult to protect, since they are sequential, highly dimensional, correlated, bound to geophysical restrictions, and easily mapped to semantic points of interest. This paper aims to establish a systematic framework on protective masking measures for trajectory databases with differentially private (DP) guarantees, including also utility properties, derived from ideas and limitations of existing proposals. To reach this goal, we systematize the utility metrics used throughout the literature, deeply analyze the DP granularity notions, explore and elaborate on the state of the art on privacy-enhancing mechanisms and their problems, and expose the main limitations of DP notions in the context of trajectories.We would like to thank the reviewers and shepherd for their useful comments and suggestions in the improvement of this paper. Javier Parra-Arnau is the recipient of a “Ramón y Cajal” fellowship funded by the Spanish Ministry of Science and Innovation. This work also received support from “la Caixa” Foundation (fellowship code LCF/BQ/PR20/11770009), the European Union’s H2020 program (Marie Skłodowska-Curie grant agreement № 847648) from the Government of Spain under the project “COMPROMISE” (PID2020-113795RB-C31/AEI/10.13039/501100011033), and from the BMBF project “PROPOLIS” (16KIS1393K). The authors at KIT are supported by KASTEL Security Research Labs (Topic 46.23 of the Helmholtz Association) and Germany’s Excellence Strategy (EXC 2050/1 ‘CeTI’; ID 390696704).Peer ReviewedPostprint (published version

SoK: Differentially Private Publication of Trajectory Data

Trajectory analysis holds many promises, from improvements in traffic management to routing advice or infrastructure development. However, learning users\u27 paths is extremely privacy-invasive. Therefore, there is a necessity to protect trajectories such that we preserve the global properties, useful for analysis, while specific and private information of individuals remains inaccessible. Trajectories, however, are difficult to protect, since they are sequential, highly dimensional, correlated, bound to geophysical restrictions, and easily mapped to semantic points of interest. This paper aims to establish a systematic framework on protective masking and synthetic-generation measures for trajectory databases with syntactic and differentially private (DP) guarantees, including also utility properties, derived from ideas and limitations of existing proposals. To reach this goal, we systematize the utility metrics used throughout the literature, deeply analyze the DP granularity notions, explore and elaborate on the state of the art on privacy-enhancing mechanisms and their problems, and expose the main limitations of DP notions in the context of trajectories