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Geo-obfuscation serves as a location privacy protection mechanism (LPPM), enabling mobile users to share obfuscated locations with servers, rather than their exact locations. This method can protect usersā€™ location privacy when data breaches occur on the server side since the obfuscation process is irreversible. To reduce the utility loss caused by data obfuscation, linear programming (LP) is widely employed, which, however, might suffer from a polynomial explosion of decision variables, rendering it impractical in largescale geo-obfuscation applications. In this paper, we propose a new LPPM, called Locally Relevant Geo-obfuscation (LR-Geo), to optimize geo-obfuscation using LP in a time-efficient manner. This is achieved by confining the geoobfuscation calculation for each user exclusively to the locally relevant (LR) locations to the userā€™s actual location. Given the potential risk of LR locations disclosing a userā€™s actual whereabouts, we enable users to compute the LP coefficients locally and upload them only to the server, rather than the LR locations. The server then solves the LP problem based on the received coefficients. Furthermore, we refine the LP framework by incorporating an exponential obfuscation mechanism to guarantee the indistinguishability of obfuscation distribution across multiple users. Based on the constraint structure of the LP formulation, we apply Bendersā€™ decomposition to further enhance computational efficiency. Our theoretical analysis confirms that, despite the geo-obfuscation being calculated independently for each user, it still meets geo-indistinguishability constraints across multiple users with high probability. Finally, the experimental results based on a real-world dataset demonstrate that LR-Geo outperforms existing geo-obfuscation methods in computational time, data utility, and privacy preservation.
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Obfuscation via Information Density Estimation
Identifying features that leak information about sensitive attributes is a key challenge in the design of information obfuscation mechanisms. In this paper, we propose a framework to identify information-leaking features via information density estimation. Here, features whose information densities exceed a pre-defined threshold are deemed information-leaking features. Once these features are identified, we sequentially pass them through a targeted obfuscation mechanism with a provable leakage guarantee in terms of š¯–¤š¯›¾-divergence. The core of this mechanism relies on a data-driven estimate of the trimmed information density for which we propose a novel estimator, named the \textit{trimmed information density estimator} (TIDE). We then use TIDE to implement our mechanism on three real-world datasets. Our approach can be used as a data-driven pipeline for designing obfuscation mechanisms targeting specific features.
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- PAR ID:
- 10202118
- Date Published:
- Journal Name:
- Proceedings of Machine Learning Research
- Volume:
- 108
- ISSN:
- 2640-3498
- Page Range / eLocation ID:
- 906-917
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
