k ‐anonymity based location privacy protection method for location‐based services in Internet of Thing

2021 ◽  
Author(s):  
Bo Wang ◽  
Yina Guo ◽  
Hongtao Li ◽  
Zhiying Li
Keyword(s):  
Privacy Protection ◽  
Location Privacy ◽  
Location Based Services ◽  
Protection Method ◽  
Location Privacy Protection ◽  
Internet Of Thing

scholarly journals A Novel Location Privacy-Preserving Approach Based on Blockchain

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3519 ◽  
Author(s):  
Ying Qiu ◽  
Yi Liu ◽  
Xuan Li ◽  
Jiahui Chen
Keyword(s):  
Privacy Protection ◽  
Location Privacy ◽  
User Participation ◽  
Location Based Services ◽  
Third Party ◽  
User Privacy ◽  
Protection Method ◽  
Location Privacy Protection ◽  
Reward Mechanism

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.

scholarly journals Location Privacy Protection Systems in Presence of Service Quality and Energy Constraints

Information ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 121
Author(s):  
Mulugeta Kassaw Tefera ◽  
Xiaolong Yang
Keyword(s):  
Service Quality ◽  
Privacy Protection ◽  
Location Privacy ◽  
Resource Consumption ◽  
Location Based Services ◽  
Design Strategies ◽  
Energy Constraints ◽  
Location Obfuscation ◽  
Evaluation Mechanism ◽  
Location Privacy Protection

The wide-ranging application of location-based services (LBSs) through the use of mobile devices and wireless networks has brought about many critical privacy challenges. To preserve the location privacy of users, most existing location privacy-preserving mechanisms (LPPMs) modify their real locations associated with different pseudonyms, which come at a cost either in terms of resource consumption or quality of service, or both. However, we observed that the effect of resource consumption has not been discussed in existing studies. In this paper, we present the user-centric LPPMs against location inference attacks under the consideration of both service quality and energy constraints. Moreover, we modeled the precision-based and dummy-based mechanisms in the context of an existing LPPM framework, and also extended the linear program solutions applicable to them. This study allowed us to specify the LPPMs that decreased the precision of exposed locations or generated dummy locations of the users. Based on this, we evaluated the privacy protection effects of optimal location obfuscation function against an adversary's inference attack function using real mobility datasets. The results indicate that dummy-based mechanisms provide better achievable location privacy under a given combination of service quality and energy constraints, and once a certain level of privacy is reached, both the precision-based and dummy-based mechanisms only perturb the exposed locations. The evaluation results also contribute to a better understanding for the LPPM design strategies and evaluation mechanism as far as the system resource utilization and service quality requirements are concerned.

scholarly journals Privacy Preserving Spatio-Temporal Databases Based on k-Anonymity

2020 ◽  
Vol 3 (SI1) ◽  
pp. SI82-SI94
Author(s):  
Anh Tuan Truong
Keyword(s):  
Data Mining ◽  
Privacy Protection ◽  
Location Privacy ◽  
Location Based Services ◽  
Sensitive Information ◽  
User Privacy ◽  
Location Data ◽  
Related Information ◽  
Spatio Temporal ◽  
Location Privacy Protection

The development of location-based services and mobile devices has lead to an increase in the location data. Through the data mining process, some valuable information can be discovered from location data. In the other words, an attacker may also extract some private (sensitive) information of the user and this may make threats against the user privacy. Therefore, location privacy protection becomes an important requirement to the success in the development of location-based services. In this paper, we propose a grid-based approach as well as an algorithm to guarantee k-anonymity, a well-known privacy protection approach, in a location database. The proposed approach considers only the information that has significance for the data mining process while ignoring the un-related information. The experiment results show the effectiveness of the proposed approach in comparison with the literature ones.

Personalized Location Privacy Protection for Location-Based Services in Vehicular Networks

2020 ◽  
Vol 9 (10) ◽  
pp. 1633-1637
Author(s):  
Chuan Xu ◽  
Li Luo ◽  
Yingyi Ding ◽  
Guofeng Zhao ◽  
Shui Yu
Keyword(s):  
Privacy Protection ◽  
Vehicular Networks ◽  
Location Privacy ◽  
Location Based Services ◽  
Location Privacy Protection

scholarly journals QBPP: Quality of services–based location privacy protection method for location-based services in cloud-enabled Internet of vehicles

2019 ◽  
Vol 15 (7) ◽  
pp. 155014771984149
Author(s):  
Ji-ming Chen ◽  
Ting-ting Li ◽  
Liang-jun Wang
Keyword(s):  
Privacy Protection ◽  
Location Privacy ◽  
Virtual Machines ◽  
Location Based Services ◽  
Security Requirements ◽  
Quality Of Services ◽  
Internet Of Vehicles ◽  
Address Data ◽  
Location Privacy Protection

Location-based services has been widely applied in cloud-enabled Internet of vehicles. Within these services, location privacy issues have captured significant attention. Vehicles use the technology of anonymity to implement occultation, the location is not revealed. In this process, large-scale data transmissions can reduce the quality of services. In order to ensure location privacy and high-quality services, the cloud manager customizes virtual machines for vehicles to support location-based services according to the vehicles’ demands. To achieve better performance, this article presents a conditional anonymity method that does not use bilinear pairings to address the problem of privacy disclosure by using discrete logarithm problem and Diffie–Hellman problem. Moreover, asymmetric key algorithms are used in the Internet of vehicles environment to reduce the cost. To guarantee secure data transmission in Internet of vehicles, the batch validation technique is used to address data integrity. Our theoretical security analysis and experiments show that the proposed scheme is secure in compared attack models, such as impersonation attacks, replay attacks, the man-in-the-middle attacks, and so on. Our proposed scheme ensures the security requirements such as message authentication, location privacy protection, and traceability, while lowering transmission and computation cost.

scholarly journals A Novel Method for Location Privacy Protection in LBS Applications

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Dan Lu ◽  
Qilong Han ◽  
Kejia Zhang ◽  
Haitao Zhang ◽  
Bisma Gull
Keyword(s):  
Service Quality ◽  
Privacy Protection ◽  
Location Privacy ◽  
Random Noise ◽  
Location Based Services ◽  
Daily Lives ◽  
Mobile Networking ◽  
Novel Method ◽  
Location Privacy Protection

Location-based services have become a mainstream in people’s daily lives due to continuous innovations in the field of mobile networking and GPS technologies. Recently they have advanced into a hot topic to which the majority of researchers pay close attention about how to enjoy them while safeguarding the location privacy of mobile users. Existing works involve the injection of random noise that cannot pledge the quality of service. Herein this manuscript, we propose a novel location privacy protection model based on the loss of service quality. This model allows the user to express his/her requirement of service quality by specifying the maximum service quality loss Lmax, which is the user’s tolerance. Lmax can be set to 0. Our comprehensive experimental evaluation using a real-world dataset demonstrates that our modus outdoes other state-of-the-art approaches.

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