A Personalized Secure Publishing Mechanism of the Sensing Location Data in Crowdsensing Location-Based Services

Yun, He; Zhang, Jiamin; Shuai, Lisha; Luo, Jingtang; Yang, Xiaolong; Sun, Qifu Tyler

doi:10.1109/jsen.2021.3070645

Cited by 6 publications

(3 citation statements)

References 19 publications

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“…The scheme [29] specifies that the personal privacy requirements of a location are negatively correlated with the number of hops to the sensitive location. The algorithm in [30] coordinates semantic privacy and location privacy based on the driver's requirements, which are measured in terms of the relationship between the drivers. Then, a game-theoretic model is constructed to protect location and differential privacy based on social distance.…”

Section: Related Workmentioning

confidence: 99%

“…According to multiattribute decision theory [30,31], the attribute whose value is positively proportional to the likelihood of a solution being chosen is called the benefit attribute; conversely, the attribute whose value is inversely proportional to the likelihood of a solution being chosen is called the cost attribute. Among the above two attributes, the Euclidean distance D between all service request locations and their nearest sensitive locations on the driving route recommended by the user in the k entry belongs to the benefit attribute.…”

Section: Multiattribute Decision Model Based On Information Entropymentioning

confidence: 99%

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PPVC: Towards a Personalized Local Differential Privacy-Preserving Scheme for V2G Charging Networks

Qin,

Wang

2023

Mathematics

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The rapid development of electric vehicles provides users with convenience of life. When users enjoy the V2G charging service, privacy leakage of their charging location is a crucial security issue. Existing privacy-preserving algorithms for EV access to charging locations suffer from the problem of nondefendable background knowledge attacks and privacy attacks by untrustworthy third parties. We propose a personalized location privacy protection scheme (PPVC) based on differential privacy to meet users’ personalized EV charging requirements while protecting their privacy. First, by constructing a decision matrix, PPVC describes recommended routes’ utility and privacy effects. Then, a utility model is constructed based on the multiattribute theory. The user’s privacy preferences are integrated into the model to provide the route with the best utility. Finally, considering the privacy preference needs of users, the Euclidean distance share is used to assign appropriate privacy budgets to users and determine the generation range of false locations to generate the service request location with the highest utility. The experimental results show that the proposed personalized location privacy protection scheme can meet the service demands of users while reasonably protecting their privacy to provide higher service quality. Compared with existing solutions, PPVC improves the charging efficiency by up to 25%, and 8% at the same privacy protection level.

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Section: Related Workmentioning

confidence: 99%

Section: Multiattribute Decision Model Based On Information Entropymentioning

confidence: 99%

PPVC: Towards a Personalized Local Differential Privacy-Preserving Scheme for V2G Charging Networks

Qin,

Wang

2023

Mathematics

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“…It effectively solves the huge pressure problem brought by massive data transmission and storage to network and cloud storage center by sinking data storage and computing to network edge [13,14]. However, as a distributed data processing method, MEC nodes may involve the mutual exchange of sensitive data in the process of cooperative data processing, which may lead to data privacy leakage [15].…”

Section: Introductionmentioning

confidence: 99%

Indoor Positioning Privacy Protection Method Based on Federated Learning in MEC Environment

Wang¹,

Tian²,

Zhu

et al. 2022

Mobile Information Systems

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Under the current background, it is very important to study the key technologies of new power system edge-to-side security protection for massive heterogeneous power IoT terminals and edge IoT agents, including defense technologies at the levels of device ontology security, communication interaction security, and secure access. Meaning. The new power system edge-to-side security protection technology has a summary impact on the privacy protection of indoor positioning. This paper proposes an indoor positioning privacy protection method based on federated learning in Mobile Edge. Computing (MEC) environment. Firstly, we analyze the learning mechanisms of horizontal, vertical, and transfer-federated learning, respectively, and mathematically describe it based on the applicability of horizontal and vertical-federated learning under different sample data characteristics. Then, the risk of data leakage when data are used for research or analysis is greatly reduced by introducing differential privacy. In addition, considering the positioning performance, privacy protection, and resource overhead, we further propose an indoor positioning privacy protection model based on federated learning and corresponding algorithms in MEC environment. Finally, through simulation experiments, the proposed algorithm and other three algorithms are, respectively, compared and analyzed in the case of two identical datasets. The experimental results show that the convergence speed, localization time consumption, and localization accuracy of the proposed algorithm are all optimal. Moreover, its final positioning accuracy is about 94%, the average positioning time is 250 ms, and the performance is better than the other three comparison algorithms.

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Spatiotemporal characteristic aware task allocation strategy using sparse user data in mobile crowdsensing

Tang

Cui²,

Yin³

et al. 2022

Wireless Netw

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A Personalized Secure Publishing Mechanism of the Sensing Location Data in Crowdsensing Location-Based Services

Cited by 6 publications

References 19 publications

PPVC: Towards a Personalized Local Differential Privacy-Preserving Scheme for V2G Charging Networks

PPVC: Towards a Personalized Local Differential Privacy-Preserving Scheme for V2G Charging Networks

Indoor Positioning Privacy Protection Method Based on Federated Learning in MEC Environment

Spatiotemporal characteristic aware task allocation strategy using sparse user data in mobile crowdsensing

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