Achieving Personalized k-Anonymity against Long-Term Observation in Location-Based Services

Li, Fenghua; Chen, Yahong; Niu, Ben; He, Yanlin; Geng, Kui; Cao, Jin

doi:10.1109/glocom.2018.8647719

Cited by 11 publications

(11 citation statements)

References 21 publications

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“…5. Comparison of effects after using our method resents the dummy selection method [4], and LODS [8]. Additionally, these two methods cannot be applied to our data directly, thereby, we keep the core ideas of these methods while some changes were made to accommodate our data.…”

Section: Quantifiable Resultsmentioning

confidence: 99%

“…Unfortunately, the existing dummy-based schemes mainly focus on protecting the user's location privacy in a single query or a whole journey within twenty-four hours. Some researchers have already considered long-term location privacy protection [8], [12], but these methods may still have some shortcomings that a region is the user's high frequency active area, not just a location. When the user adopts these schemes to protect his location privacy in consecutive requests, and we utilize the hypothetical adversary method in Sec IV to attack, some dummies can be inferred with no less than 25% correct ratio.…”

Section: Related Workmentioning

confidence: 99%

“…Existing dummy-based method is mainly concerned with a short period(e.g., a single query or a whole journey within twenty-four hours). In practice, however, longterm requests still exist [8], [12]. For example, a user makes requests at home for up to 30 days.…”

Section: Introductionmentioning

confidence: 99%

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FADBM: Frequency-Aware Dummy-Based Method in Long-Term Location Privacy Protection

Liu

Jiang

Zhang

et al. 2019

2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS)

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With the rapid usage of location-based services (LBSs), protection of location privacy has become a significant concern. Existing dummy-based methods mainly consider generating dummies in continuous queries, which neglects the fact that the user would launch queries in a frequent region(e.g., home), resulting in privacy disclosure in a long-term period (i.e., more than 30 days). To solve this problem, we propose a method which generates dummy frequent regions, and make dummies locate in these dummy regions as far as possible. Compared with other methods, evaluation based on real-world dataset shows that the proposed method can reduce the ratio of recognized dummies and restored trajectory in a long-term situation.

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Section: Quantifiable Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

FADBM: Frequency-Aware Dummy-Based Method in Long-Term Location Privacy Protection

Liu

Jiang

Zhang

et al. 2019

2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS)

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show abstract

“…While strengthening the location privacy, this solution still operates at higher layers, leaving open the possibility of localizing and identifying users by physical layer techniques, as detailed in the following. Other approaches resort to k-anonymity [ 6 ] or differential privacy [ 7 ]. In [ 8 ], the direct connection between the location service provider and the user is cut by resorting to a multi-server architecture and a differential privacy approach.…”

Section: Literature Backgroundmentioning

confidence: 99%

Location-Privacy Leakage and Integrated Solutions for 5G Cellular Networks and Beyond

Tomasin

Centenaro

Seco‐Granados

et al. 2021

Sensors

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The 5g of cellular networks improves the precision of user localization and provides the means to disclose location information to ott service providers. The nwdaf can further elaborate this information at an aggregated level using artificial intelligence techniques. These powerful features may lead to the improper use of user location information by mno and ott service providers. Moreover, vulnerabilities at various layers may also leak user location information to eavesdroppers. Hence, the privacy of users is likely at risk, as location is part of their sensitive data. In this paper, we first go through the evolution of localization in cellular networks and investigate their effects on location privacy. Then, we propose a location-privacy-preserving integrated solution comprising virtual private mobile networks, an independent authentication and billing authority, and functions to protect wireless signals against location information leakage. Moreover, we advocate the continuous and detailed control of localization services by the user.

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“…In the direction of spatial obfuscation, Gruteser et al [ 23 ] first used the k -anonymity mechanism to obfuscate the user’s location in the anonymizing servers. These methods of generalizing the user’s location into a larger area or adding more other locations [ 24 ] will reduce the accuracy of the location query, resulting in a reduction in the quality of location services. Similar to spatial obfuscation, location perturbations will also reduce the quality of location services.…”

Section: Related Workmentioning

confidence: 99%

A Novel Location Privacy-Preserving Approach Based on Blockchain

Qiu

Liu

et al. 2020

Sensors

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Location-based services (LBS) bring convenience to people’s lives but are also accompanied with privacy leakages. To protect the privacy of LBS users, many location privacy protection algorithms were proposed. However, these algorithms often have difficulty to maintain a balance between service quality and user privacy. In this paper, we first overview the shortcomings of the existing two privacy protection architectures and privacy protection technologies, then we propose a location privacy protection method based on blockchain. Our method satisfies the principle of k-anonymity privacy protection and does not need the help of trusted third-party anonymizing servers. The combination of multiple private blockchains can disperse the user’s transaction records, which can provide users with stronger location privacy protection and will not reduce the quality of service. We also propose a reward mechanism to encourage user participation. Finally, we implement our approach in the Remix blockchain to show the efficiency, which further indicates the potential application prospect for the distributed network environment.

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Achieving Personalized k-Anonymity against Long-Term Observation in Location-Based Services

Cited by 11 publications

References 21 publications

FADBM: Frequency-Aware Dummy-Based Method in Long-Term Location Privacy Protection

FADBM: Frequency-Aware Dummy-Based Method in Long-Term Location Privacy Protection

Location-Privacy Leakage and Integrated Solutions for 5G Cellular Networks and Beyond

A Novel Location Privacy-Preserving Approach Based on Blockchain

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