Location Privacy Preservation Mechanism for Location-Based Service With Incomplete Location Data

Yang, Xudong; Gao, Ling; Zheng, Jing; Wei, Wei

doi:10.1109/access.2020.2995504

Cited by 26 publications

(19 citation statements)

References 31 publications

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“…Hilbert curve can be used to build a set of candidate cloaking regions as adopted in ( [40], [41], and [42], by rotating and shifting the Hilbert curve around the centre point of the data space. A randomization algorithm (such as the exponential mechanism) is used to selected from the cloaking regions candidates using a score function (such as the cloaking region size).…”

Section: Anonymity-based Methodsmentioning

confidence: 99%

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An Analysis of Differential Privacy Research in Location and Trajectory Data

Errounda

Liu²

2020

Preprint

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Location and trajectory data are routinely collected to generate valuable knowledge about users' pattern behavior. However, releasing location data may jeopardize the privacy of the involved individuals. Differential privacy is a powerful technique that prevents an adversary from inferring the presence or absence of an individual in the original data solely based on the observed data. The first challenge in applying differential privacy in location is that a it usually involves a single user. This shifts the adversary's target to the user's locations instead of presence or absence in the original data. The second challenge is that the inherent correlation between location data, due to people's movement regularity and predictability, gives the adversary an advantage in inferring information about individuals. In this paper, we review the differentially private approaches to tackle these challenges. Our goal is to help newcomers to the field to better understand the state-of-the art by providing a research map that highlights the different challenges in designing differentially private frameworks that tackle the characteristics of location data. We find that in protecting an individual's location privacy, the attention of differential privacy mechanisms shifts to preventing the adversary from inferring the original location based on the observed one. Moreover, we find that the privacy-preserving mechanisms make use of the predictability and regularity of users' movements to design and protect the users' privacy in trajectory data. Finally, we explore how well the presented frameworks succeed in protecting users' locations and trajectories against well-known privacy attacks.

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Section: Anonymity-based Methodsmentioning

confidence: 99%

“…Anonymity-based methods [40], [41], and [42] The indistinguishability is between locations of multiple users (C3) The need for an anonymizer that collects the locations of multiple users may create privacy attack opportunities.…”

Section: Challengesmentioning

confidence: 99%

An Analysis of Differential Privacy Research in Location and Trajectory Data

Errounda

Liu²

2020

Preprint

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“…Zhang et al generated dummy locations by taking the road network correlations into account [28]. Yang et al [29] considered the reachability constraint, i.e., a type of temporal correlation between continually released locations when generating the dummy locations. In addition, to protect users' real locations against inference attacks based on location semantics, Sun et al [30] generated dummy locations that shared nearly equivalent query probabilities.…”

Section: A Protection Of Location Privacymentioning

confidence: 99%

“…In addition, the model is built based on the obfuscated trajectory itself, resulting in a dependency on the LPPM used to generate the obfuscated trajectory. Other attacks exploit temporal correlations, such as continuity of a user's movement [12], [29], [35] and road constraints [19].…”

Section: B Attacks On Location Privacymentioning

confidence: 99%

Quantifying Location Privacy Risks Under Heterogeneous Correlations

Zhu

Xie

2021

IEEE Access

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Currently, increasingly ubiquitous location-based services are facilitating the activities of people in daily life. However, releasing real locations could lead to serious concerns about privacy. To remedy these issues, a number of location privacy protection mechanisms (LPPMs) have been proposed, e.g., spatial cloaking, dummy location generation, query caching, and perturbation. However, these LPPMs are vulnerable to inference attacks because of the incompleteness of the captured privacy risks caused by heterogeneous correlations in location data, e.g., semantical, temporal, and social correlations. Consequently, they cannot provide sufficient privacy guarantees due to the absence of embedded heterogeneous correlations in the design process of LPPM. To address these issues, we present QUAD, a framework for quantifying location privacy risks under heterogeneous correlations. QUAD has three features: (i) it enables the modeling and seamless fusion of multiple kinds of correlations that are available to adversaries; (ii) it provides a probabilistic representation of the privacy risks faced under heterogeneous correlations; and (iii) it achieves the quantification of privacy risks for multiple kinds of LPPMs that are widely used in the literature. To mitigate privacy threats, we propose a defense mechanism embedded with the quantified privacy risks. Extensive experiments on two real-world datasets confirm that QUAD can capture more privacy risks than competitors, and the risks can be dramatically reduced by our defense mechanism.

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“…At the same time, the generation of dummy locations does not need a trustable third-party server. In recent years, many location privacy-preservation methods based on dummy locations have been proposed [15][16][17][18][19][20][21][22][23]. However, due to the characteristics of vehicles, the location of vehicles is subject to the road distribution, many methods cannot be directly adopted in IoV.…”

Section: Introductionmentioning

confidence: 99%

A Location Privacy Preservation Method Based on Dummy Locations in Internet of Vehicles

Chen

Xie

2021

Applied Sciences

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During the procedure, a location-based service (LBS) query, the real location provided by the vehicle user may results in the disclosure of vehicle location privacy. Moreover, the point of interest retrieval service requires high accuracy of location information. However, some privacy preservation methods based on anonymity or obfuscation will affect the service quality. Hence, we study the location privacy-preserving method based on dummy locations in this paper. We propose a vehicle location privacy-preservation method based on dummy locations under road restriction in Internet of vehicles (IoV). In order to improve the validity of selected dummy locations under road restriction, entropy is used to represent the degree of anonymity, and the effective distance is introduced to represent the characteristics of location distribution. We present a dummy location selection algorithm to maximize the anonymous entropy and the effective distance of candidate location set consisting of vehicle user’s location and dummy locations, which ensures the uncertainty and dispersion of selected dummy locations. The proposed location privacy-preservation method does not need a trustable third-party server, and it protects the location privacy of vehicles as well as guaranteeing the LBS quality. The performance analysis and simulation results show that the proposed location privacy-preservation method can improve the validity of dummy locations and enhance the preservation of location privacy compared with other methods based on dummy locations.

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Location Privacy Preservation Mechanism for Location-Based Service With Incomplete Location Data

Cited by 26 publications

References 31 publications

An Analysis of Differential Privacy Research in Location and Trajectory Data

An Analysis of Differential Privacy Research in Location and Trajectory Data

Quantifying Location Privacy Risks Under Heterogeneous Correlations

A Location Privacy Preservation Method Based on Dummy Locations in Internet of Vehicles

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