Abstract
Communication devices with GPS chips allow people to generate large volumes of location data. However, location datasets have been confronted with serious privacy concerns. Recently, several privacy techniques have been proposed but most of them lack a strict privacy notion, and can hardly resist the number of possible attacks. This paper proposes a private release algorithm to randomize location datasets in a strict privacy notion, differential privacy. This algorithm includes three privacy-preserving operations: Private Location Clustering shrinks the randomized domain and Cluster Weight Perturbation hides the weights of locations, while Private Location Selection hides the exact locations of a user. Theoretical analysis on utility confirms an improved trade-off between the privacy and utility of released location data. The experimental results further suggest this private release algorithm can successfully retain the utility of the datasets while preserving users’ privacy.
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Xiong, P., Zhu, T., Pan, L., Niu, W., Li, G. (2014). Privacy Preserving in Location Data Release: A Differential Privacy Approach. In: Pham, DN., Park, SB. (eds) PRICAI 2014: Trends in Artificial Intelligence. PRICAI 2014. Lecture Notes in Computer Science(), vol 8862. Springer, Cham. https://doi.org/10.1007/978-3-319-13560-1_15
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DOI: https://doi.org/10.1007/978-3-319-13560-1_15
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13559-5
Online ISBN: 978-3-319-13560-1
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