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On-the-fly privacy for location histograms

Theodorakopoulos, George ORCID:, Panaousis, Emmanouil, Liang, Kaitai and Loukas, George 2022. On-the-fly privacy for location histograms. IEEE Transactions on Dependable and Secure Computing 19 (1) , pp. 566-578. 10.1109/TDSC.2020.2980270

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An important motivation for research in location privacy has been to protect against user profiling, i.e., inferring a user's political affiliation, wealth level, sexual preferences, religious beliefs and other sensitive attributes. Existing approaches focus on distorting or suppressing individual locations, but we argue that, for directly protecting against profiling, it is more appropriate to focus on the frequency with which various locations are visited -- in other words, the histogram of a user's locations. We introduce and explore a new privacy notion, namely, on-the-fly privacy for location histograms, in which a mobile user repeatedly submits obfuscated locations to a Location-Based Service aiming for the resulting histogram to resemble a target profile or differ from it. For example, she may want to avoid looking wealthy or to resemble a health conscious person. We describe how to design concrete privacy mechanisms that operate under different assumptions on, e.g., the user's mobility, including provably optimal mechanisms. We use a mobility dataset with 1083 users to illustrate how these mechanisms achieve privacy while minimizing the quality loss caused by the location obfuscation, in the context of two types of Location-Based Services: nearest-PoI, and geofence.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher: IEEE
ISSN: 1545-5971
Date of First Compliant Deposit: 10 March 2020
Date of Acceptance: 7 March 2020
Last Modified: 07 Nov 2023 02:59

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