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http://hdl.handle.net/10609/122026
Title: Swapping trajectories with a sufficient sanitizer
Author: Salas Piñón, Julián
Megías Jiménez, David  
Torra Reventós, Vicenç
Toger, Marina
Dahne, Joel
Sainudiin, Raazesh
Others: Universitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3)
Maynooth University
University of Skövde
Uppsala University
Keywords: privacy preserving mobility data mining
real-time mobility data anonymization
trajectory anonymization
sufficient sanitizer
intelligent transportation systems
origin-Destination matrices
Issue Date: 2-Mar-2020
Publisher: Pattern Recognition Letters
Citation: Salas, J., Megías, D., Torra, V., Toger, M., Dahne, J. & Sainudiin, R. (2020). Swapping trajectories with a sufficient sanitizer. Pattern Recognition Letters, 131(), 474-480. doi: 10.1016/j.patrec.2020.02.011
Project identifier: info:eu-repo/grantAgreement/RTI2018-095094-B-C22
info:eu-repo/grantAgreement/TIN2014-57364-C2-2-R
Also see: https://doi.org/10.1016/j.patrec.2020.02.011
Abstract: Real-time mobility data is useful for several applications such as planning transports in metropolitan ar- eas or localizing services in towns. However, if such data is collected without any privacy protection it may reveal sensible locations and pose safety risks to an individual associated to it. Thus, mobility data must be anonymized preferably at the time of collection. In this paper, we consider the SwapMob algo- rithm that mitigates privacy risks by swapping partial trajectories. We formalize the concept of sufficient sanitizer and show that the SwapMob algorithm is a sufficient sanitizer for various statistical decision problems. That is, it preserves the aggregate information of the spatial database in the form of sufficient statistics and also provides privacy to the individuals. This may be used for personalized assistants taking advantage of users' locations, so they can ensure user privacy while providing accurate response to the user requirements. We measure the privacy provided by SwapMob as the Adversary Information Gain, which measures the capability of an adversary to leverage his knowledge of exact data points to infer a larger segment of the sanitized trajectory. We test the utility of the data obtained after applying Swap- Mob sanitization in terms of Origin-Destination matrices, a fundamental tool in transportation modelling.
Language: English
URI: http://hdl.handle.net/10609/122026
ISSN: 0167-8655MIAR
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