Differential Privacy Location Protection Method Based on the Markov Model

Li, Hongtao; Wang, Yue; Feng, Guocan; Wang, Jie; Wang, Bo; Wu, Changchun

doi:10.1155/2021/4696455

Cited by 9 publications

(6 citation statements)

References 29 publications

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“…Zhang et al [29] protected users' location privacy through the Laplace mechanism and used the indexing mechanism to protect the query privacy of users. Li et al [30] used the Markov model to predict the user location and added Laplace noise to the two nodes with the largest predicted values to protect user location. Zhang et al [31] used differential privacy to protect user trajectory data.…”

Section: Differential Privacymentioning

confidence: 99%

“…Definition 2. Global sensitivity [27,30]. Global sensitivity is an important metric for differential privacy-preserving algorithms.…”

Section: Differential Privacymentioning

confidence: 99%

“…Definition 3. Laplace mechanism [30]. Given a location dataset D, for any function f: D → R d with sensitivity ∆f, if the output of function f satisfies Equation ( 5):…”

Section: Differential Privacymentioning

confidence: 99%

“…The Gowalla dataset and the Geolife dataset [30] were used to verify the performance of the designed scheme. Gowalla collects user locations through their check-ins and contains 6,442,890 check-in data.…”

Section: Experimental Simulation Settingsmentioning

confidence: 99%

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Cache-Based Privacy Protection Scheme for Continuous Location Query

Liu

Miao

et al. 2023

Entropy

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Users who initiate continuous location queries are prone to trajectory information leakage, and the obtained query information is not effectively utilized. To address these problems, we propose a continuous location query protection scheme based on caching and an adaptive variable-order Markov model. When a user initiates a query request, we first query the cache information to obtain the required data. When the local cache cannot satisfy the user’s demand, we use a variable-order Markov model to predict the user’s future query location and generate a k-anonymous set based on the predicted location and cache contribution. We perturb the location set using differential privacy, then send the perturbed location set to the location service provider to obtain the service. We cache the query results returned by the service provider to the local device and update the local cache results according to time. By comparing the experiment with other schemes, the proposed scheme in this paper reduces the number of interactions with location providers, improves the local cache hit rate, and effectively ensures the security of the users’ location privacy.

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Section: Differential Privacymentioning

confidence: 99%

“…Definition 2. Global sensitivity [27,30]. Global sensitivity is an important metric for differential privacy-preserving algorithms.…”

Section: Differential Privacymentioning

confidence: 99%

“…Definition 3. Laplace mechanism [30]. Given a location dataset D, for any function f: D → R d with sensitivity ∆f, if the output of function f satisfies Equation ( 5):…”

Section: Differential Privacymentioning

confidence: 99%

Section: Experimental Simulation Settingsmentioning

confidence: 99%

See 2 more Smart Citations

Cache-Based Privacy Protection Scheme for Continuous Location Query

Liu

Miao

et al. 2023

Entropy

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“…Differential privacy protects location privacy by adding an appropriate number of noises to the returned value of the query function [29]. Several recent studies [32,33] have investigated the use of differential privacy in location protection. The concept of protecting user locations within a radius R, whose privacy level is dependent on R, is formally defined by the term of geographical indiscernibility [32].…”

Section: Location Privacy Protection Techniques Based On Obfuscationmentioning

confidence: 99%

A Survey of Dummy-Based Location Privacy Protection Techniques for Location-Based Services

Zhang

Liang

et al. 2022

Sensors

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As smart devices and mobile positioning technologies improve, location-based services (LBS) have grown in popularity. The LBS environment provides considerable convenience to users, but it also poses a significant threat to their privacy. A large number of research works have emerged to protect users’ privacy. Dummy-based location privacy protection solutions have been widely adopted for their simplicity and enhanced privacy protection results, but there are few reviews on dummy-based location privacy protection. Or, for existing works, some focus on aspects of cryptography, anonymity, or other comprehensive reviews that do not provide enough reviews on dummy-based privacy protection. In this paper, the authors provide a review of dummy-based location privacy protection techniques for location-based services. More specifically, the connection between the level of privacy protection, the quality of service, and the system overhead is summarized. The difference and connection between various location privacy protection techniques are also described. The dummy-based attack models are presented. Then, the algorithms for dummy location selection are analyzed and evaluated. Finally, we thoroughly evaluate different dummy location selection methods and arrive at a highly useful evaluation result. This result is valuable both to users and researchers who are studying this field.

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