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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 users’ 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 user’s actual location. Given the potential risk of LR locations disclosing a user’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 Benders’ 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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Privacy-preserving Travel Time Prediction with Uncertainty Using GPS Trace Data
The rapid growth of GPS technology and mobile devices has led to a massive accumulation of location data, bringing considerable benefits to individuals and society. One of the major usages of such data is travel time prediction, a typical service provided by GPS navigation devices and apps. Meanwhile, the constant collection and analysis of the individual location data also pose unprecedented privacy threats. We leverage the notion of geo-indistinguishability, an extension of differential privacy to the location privacy setting, and propose a procedure for privacy-preserving travel time prediction without collecting actual individual GPS trace data. We propose new concepts to examine the impact of the geo-indistinguishability sanitization on the usefulness of GPS traces and provide analytical and experimental utility analysis for privacy-preserving travel time prediction. We also propose new metrics to measure the adversary error in learning individual GPS traces from the collected sanitized data. Our experiment results suggest that the proposed procedure provides travel time analysis with satisfactory accuracy at reasonably small privacy costs.
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- Award ID(s):
- 1717417
- PAR ID:
- 10311800
- Date Published:
- Journal Name:
- IEEE Transactions on Mobile Computing
- ISSN:
- 1536-1233
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Geo-obfuscation is a location privacy protection mechanism used by mobile users to conceal their precise locations when reporting location data, and it has been widely used to protect the location privacy of workers in spatial crowdsourcing (SC). However, this technique introduces inaccuracies in the reported locations, raising the question of how to control the quality loss that results from obfuscation in SC services. Prior studies have addressed this issue in time-insensitive SC settings, where some degree of quality degradation can be accepted and the locations can be expressed with less precision, which, however, is inadequate for time-sensitive SC. In this paper, we aim to minimize the quality loss caused by geo-obfuscation in time-sensitive SC applications. To this end, we model workers’ mobility on a fine-grained location field and constrain each worker’s obfuscation range to a set of peer locations, which have similar traveling costs to the destination as the actual location. We apply a linear programming (LP) framework to minimize the quality loss while satisfying both peer location constraints and geo-indistinguishability, a location privacy criterion extended from differential privacy. By leveraging the constraint features of the formulated LP, we enhance the time efficiency of solving LP through the geo-indistinguishability constraint reduction and the column generation algorithm. Using both simulation and real-world experiments, we demonstrate that our approach can reduce the quality loss of SC applications while protecting workers’ location privacy.more » « less
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The emergence of mobile apps (e.g., location-based services,geo-social networks, ride-sharing) led to the collection of vast amounts of trajectory data that greatly benefit the understanding of individual mobility. One problem of particular interest is next-location prediction, which facilitates location-based advertising, point-of-interest recommendation, traffic optimization,etc. However, using individual trajectories to build prediction models introduces serious privacy concerns, since exact whereabouts of users can disclose sensitive information such as their health status or lifestyle choices. Several research efforts focused on privacy-preserving next-location prediction, but they have serious limitations: some use outdated privacy models (e.g., k-anonymity), while others employ learning models with limited expressivity (e.g., matrix factorization). More recent approaches(e.g., DP-SGD) integrate the powerful differential privacy model with neural networks, but they provide only generic and difficult-to-tune methods that do not perform well on location data, which is inherently skewed and sparse.We propose a technique that builds upon DP-SGD, but adapts it for the requirements of next-location prediction. We focus on user-level privacy, a strong privacy guarantee that protects users regardless of how much data they contribute. Central toour approach is the use of the skip-gram model, and its negative sampling technique. Our work is the first to propose differentially-private learning with skip-grams. In addition, we devise data grouping techniques within the skip-gram framework that pool together trajectories from multiple users in order to acceleratelearning and improve model accuracy. Experiments conducted on real datasets demonstrate that our approach significantly boosts prediction accuracy compared to existing DP-SGD techniques.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TMC.2021.3074865
