TrajectGuard: a comprehensive privacy-risk framework for multiple-aspects trajectories

Gomes F. O., Pellungrini R., Monreale A., Renso C., Martina J. E.

Data privacy  TK1-9971  re-identification  privacy  Re-identification  Internet of Things  Human mobility  trajectory  Privacy risk assessment  privacy risk  Privacy  human mobility  Social networking (online)  Privacy risk  Semantics  Multiple-aspects trajectories  Trajectory  Electrical engineering. Electronics. Nuclear engineering  privacy risk assessment  Risk management  multiple-aspects trajectories 

With the rise of the Internet of Things (IoT), social networks, and mobile devices, vast amounts of mobility data are continuously generated. These data encompass diverse location information from various sources, including smart vehicles, sensors, wearables, and social media platforms. By leveraging these data, we explore the semantic enrichment of trajectory components related to moving objects and locations, bringing the so-called multiple-aspects trajectories and relative privacy issues. Privacy risk analysis is crucial for the earlier detection of privacy problems, particularly when dealing with semantically enriched trajectories. In this study, we introduced the TrajectGuard privacy risk assessment framework. TrajectGuard, an extension of PRUDEnce, achieved significant results by formulating and assessing the privacy risk of multiple-aspects trajectories under several proposed attacks. The framework introduced a nuanced risk evaluation using AspectGuard and conducted fair privacy assessments on anonymized datasets using AnonimoGuard. Its adaptability and versatility make TrajectGuard a valuable tool for preserving data privacy with multiple-aspects.

Source: IEEE ACCESS, vol. 12, pp. 136354-136378

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