6,319 research outputs found

    On the Anonymization of Differentially Private Location Obfuscation

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    Obfuscation techniques in location-based services (LBSs) have been shown useful to hide the concrete locations of service users, whereas they do not necessarily provide the anonymity. We quantify the anonymity of the location data obfuscated by the planar Laplacian mechanism and that by the optimal geo-indistinguishable mechanism of Bordenabe et al. We empirically show that the latter provides stronger anonymity than the former in the sense that more users in the database satisfy k-anonymity. To formalize and analyze such approximate anonymity we introduce the notion of asymptotic anonymity. Then we show that the location data obfuscated by the optimal geo-indistinguishable mechanism can be anonymized by removing a smaller number of users from the database. Furthermore, we demonstrate that the optimal geo-indistinguishable mechanism has better utility both for users and for data analysts.Comment: ISITA'18 conference pape

    Optimal Geo-Indistinguishable Mechanisms for Location Privacy

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    We consider the geo-indistinguishability approach to location privacy, and the trade-off with respect to utility. We show that, given a desired degree of geo-indistinguishability, it is possible to construct a mechanism that minimizes the service quality loss, using linear programming techniques. In addition we show that, under certain conditions, such mechanism also provides optimal privacy in the sense of Shokri et al. Furthermore, we propose a method to reduce the number of constraints of the linear program from cubic to quadratic, maintaining the privacy guarantees and without affecting significantly the utility of the generated mechanism. This reduces considerably the time required to solve the linear program, thus enlarging significantly the location sets for which the optimal mechanisms can be computed.Comment: 13 page

    No Place to Hide that Bytes won't Reveal: Sniffing Location-Based Encrypted Traffic to Track a User's Position

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    News reports of the last few years indicated that several intelligence agencies are able to monitor large networks or entire portions of the Internet backbone. Such a powerful adversary has only recently been considered by the academic literature. In this paper, we propose a new adversary model for Location Based Services (LBSs). The model takes into account an unauthorized third party, different from the LBS provider itself, that wants to infer the location and monitor the movements of a LBS user. We show that such an adversary can extrapolate the position of a target user by just analyzing the size and the timing of the encrypted traffic exchanged between that user and the LBS provider. We performed a thorough analysis of a widely deployed location based app that comes pre-installed with many Android devices: GoogleNow. The results are encouraging and highlight the importance of devising more effective countermeasures against powerful adversaries to preserve the privacy of LBS users.Comment: 14 pages, 9th International Conference on Network and System Security (NSS 2015

    ReverseCloak: A Reversible Multi-level Location Privacy Protection System

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    With the fast popularization of mobile devices and wireless networks, along with advances in sensing and positioning technology, we are witnessing a huge proliferation of Location-based Services (LBSs). Location anonymization refers to the process of perturbing the exact location of LBS users as a cloaking region such that a user's location becomes indistinguishable from the location of a set of other users. However, existing location anonymization techniques focus primarily on single level unidirectional anonymization, which fails to control the access to the cloaking data to let data requesters with different privileges get information with varying degrees of anonymity. In this demonstration, we present a toolkit for ReverseCloak, a location perturbation system to protect location privacy over road networks in a multi-level reversible manner, consisting of an 'Anonymizer' GUI to adjust the anonymization settings and visualize the multilevel cloaking regions over road network for location data owners and a 'De-anonymizer' GUI to de-anonymize the cloaking region and display the reduced region over road network for location data requesters. With the toolkit, we demonstrate the practicality and effectiveness of the ReverseCloak approach
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