An incentive mechanism for K-anonymity in LBS privacy protection based on credit mechanism

Li, Xinghua; Miao, Meixia; Liu, Hai; Ma, Jianfeng; Li, Kuan‐Ching

doi:10.1007/s00500-016-2040-2

Cited by 45 publications

(30 citation statements)

References 19 publications

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“…In [16], the authors present an incentive-based batch algorithm to build a K-anonymity covering with K − 1 willing participants. In this work, a probability threshold is suggested to indicate a user's reputation on a framework based on fuzzy logic.…”

Section: Mobile Information Systemsmentioning

confidence: 99%

“…Most of the approaches [2][3][4][5][6][7][8][9][10][11] are based on a trusted third party, called the anonymizer, which is responsible for selecting these additional locations depending on what type of protection a user is demanding. Other techniques such as [12][13][14][15][16] assume that the same users, collaborating with other peers, can compute their own cloaking regions. In addition, a few articles have proposed a hybrid approach, in which an anonymizer and users collaborate to create cloaking regions [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Batching Location Cloaking Techniques for Location Privacy and Safety Protection

Galdames

Gutiérrez-Soto

Curiel

2019

Mobile Information Systems

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Location-based services (LBSs) have become a profitable market because they offer real-time and local information to their users. Although several benefits are obtained from the usage of LBSs, they have opened up many privacy and safety challenges because a user needs to release his/her location. To tackle these challenges, many location-cloaking techniques have been proposed. Even though these solutions are effective in protecting either location privacy or location safety, they do not provide unified protection. Furthermore, most of them do not address the potential bottleneck in the anonymity server as a high demand of location and safety protection is requested. Finally, they do not take into account the potential impact of processing a large amount of location-cloaked queries. This paper deals with the efficient construction of location-cloaking areas for many users, who have both privacy and safety requirements. To achieve this goal, the construction of location-cloaking areas is carried out in batches. The LBSs’ batch processing takes advantage of users who are close to each other and who have similar requirements. Two batching techniques to build cloaking regions are analyzed using simulations. Empirical results show our techniques are able to balance the anonymizer workload, quality of location privacy and safety protection, and LBS workload.

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Section: Mobile Information Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Batching Location Cloaking Techniques for Location Privacy and Safety Protection

Galdames

Gutiérrez-Soto

Curiel

2019

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…In the location privacy protection approach put forward by Sun G et al [18], location labels are adopted to distinguish mobile users' sensitive locations and ordinary locations, both of which are protected at different levels, thus the response efficiency of LBS requests is improved. According to Li et al [19], to improve the efficiency of selecting dummy locations, an credit-incentive mechanism is introduced in K -anonymity scheme. Based on fuzzy logic, each user's credit level corresponds to certain probability threshold value.…”

Section: Related Workmentioning

confidence: 99%

A Trajectory Substitution Privacy Protection Scheme in location-based services

2019

KSII TIIS

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Aimed at the disclosure risk of mobile terminal user's location privacy in location-based services, a location-privacy protection scheme based on similar trajectory substitution is proposed. On the basis of the anonymized identities of users and candidates who request LBS, this scheme adopts trajectory similarity function to select the candidate whose trajectory is the most similar to user's at certain time intervals, then the selected candidate substitutes user to send LBS request, so as to protect user's privacy like identity, query and trajectory. Security analyses prove that this scheme is able to guarantee such security features as anonymity, non-forgeability, resistance to continuous query tracing attack and wiretapping attack. And the results of simulation experiment demonstrate that this scheme remarkably improve the optimal candidate' trajectory similarity and selection efficiency.

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“…In recent years, many techniques have been proposed to solve the problem of location privacy protection, such as location perturbation and obfuscation [18,19], region anonymization [20][21][22], and dummy location [23]. The region anonymization technique, which reduces the probability of identifying the real user to 1/k, is an important one for location privacy protection.…”

Section: Introductionmentioning

confidence: 99%

An anonymous entropy-based location privacy protection scheme in mobile social networks

Tian

et al. 2019

J Wireless Com Network

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The popularization of mobile communication devices and location technology has spurred the increasing demand for location-based services (LBSs). While enjoying the convenience provided by LBS, users may be confronted with the risk of privacy leakage. It is very crucial to devise a secure scheme to protect the location privacy of users. In this paper, we propose an anonymous entropy-based location privacy protection scheme in mobile social networks (MSN), which includes two algorithms K-DDCA in a densely populated region and K-SDCA in a sparsely populated region to tackle the problem of location privacy leakage. The K-DDCA algorithm employs anonymous entropy method to select user groups and construct anonymous regions which can guarantee the area of the anonymous region formed be moderate and the diversity of the request content. The K-SDCA algorithm generates a set of similar dummy locations which can resist the attack of adversaries with background information. Particularly, we present the anonymous entropy method based on the location distance and request contents. The effectiveness of our scheme is validated through extensive simulations, which show that our scheme can achieve enhanced privacy preservation and better efficiency.

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An incentive mechanism for K-anonymity in LBS privacy protection based on credit mechanism

Cited by 45 publications

References 19 publications

Batching Location Cloaking Techniques for Location Privacy and Safety Protection

Batching Location Cloaking Techniques for Location Privacy and Safety Protection

A Trajectory Substitution Privacy Protection Scheme in location-based services

An anonymous entropy-based location privacy protection scheme in mobile social networks

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