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Gotowe bibliografie tematyczne / Location- Privacy control

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Artykuły w czasopismach

Gotowa bibliografia na temat „Location- Privacy control”

Autor: Grafiati

Data publikacji: 6 września 2023

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Artykuły w czasopismach na temat "Location- Privacy control"

1

Yunxiang, Zhang, and Wang Bin. "Stability Control of Position Flow Fuzzy Estimation in Swarm Intelligence Aware Privacy Protection." Wireless Communications and Mobile Computing 2023 (January 30, 2023): 1–8. http://dx.doi.org/10.1155/2023/4792248.

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The group intelligence perception privacy protection model is a method to achieve the balance between user privacy and service requests through the cooperation between users using location services and has a good perception effect. In order to better protect the location privacy of network users and improve the stability control effect of fuzzy estimation of location flow, this paper designs a stability control method of fuzzy estimation of location flow in group intelligent perception privacy protection. This method uses the group intelligence aware privacy protection model to obtain the user network location coordinates in the group intelligence aware privacy protection. Taking the user’s network location coordinates as input, the location flow queue of multiple users in the group intelligence aware privacy protection network is obtained by the Lyapunov multiobjective location flow estimation queue model. After the fuzzy processing of the user location flow queue, the online control mechanism of location flow fuzzy estimation stability under different conditions is established. According to the online control mechanism, a stability control method based on access control and group intelligence aware task allocation is used to realize the stability control of location flow fuzzy estimation in group intelligence aware privacy protection. The experimental results show that the method can obtain 100% of the user location integrity in the group intelligence aware privacy protection, and the target location flow estimation queue is more accurate. It can effectively reduce the number of communication rounds of fuzzy estimation of location flow in the group intelligence aware privacy protection and has better stability control ability.

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2

Peng, Tao, Qin Liu, and Guojun Wang. "Enhanced Location Privacy Preserving Scheme in Location-Based Services." IEEE Systems Journal 11, no. 1 (2017): 219–30. http://dx.doi.org/10.1109/jsyst.2014.2354235.

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3

Zhu, Liang, Xiaowei Liu, Zhiyong Jing, Liping Yu, Zengyu Cai, and Jianwei Zhang. "Knowledge-Driven Location Privacy Preserving Scheme for Location-Based Social Networks." Electronics 12, no. 1 (2022): 70. http://dx.doi.org/10.3390/electronics12010070.

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Location privacy-preserving methods for location-based services in mobile communication networks have received great attention. Traditional location privacy-preserving methods mostly focus on the researches of location data analysis in geographical space. However, there is a lack of studies on location privacy preservation by considering the personalized features of users. In this paper, we present a Knowledge-Driven Location Privacy Preserving (KD-LPP) scheme, in order to mine user preferences and provide customized location privacy protection for users. Firstly, the UBPG algorithm is proposed to mine the basic portrait. User familiarity and user curiosity are modelled to generate psychological portrait. Then, the location transfer matrix based on the user portrait is built to transfer the real location to an anonymous location. In order to achieve customized privacy protection, the amount of privacy is modelled to quantize the demand of privacy protection of target user. Finally, experimental evaluation on two real datasets illustrates that our KD-LPP scheme can not only protect user privacy, but also achieve better accuracy of privacy protection.

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4

Yang, Guangcan, Shoushan Luo, Yang Xin, et al. "A Search Efficient Privacy-Preserving Location-Sharing Scheme in Mobile Online Social Networks." Applied Sciences 10, no. 23 (2020): 8402. http://dx.doi.org/10.3390/app10238402.

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With the advent of intelligent handheld devices, location sharing becomes one of the most popular services in mobile online social networks (mOSNs). In location-sharing services, users can enjoy a better social experience by updating their real-time location information. However, the leakage of private information may hinder the further development of location-sharing services. Although many solutions have been proposed to protect users’ privacy, the privacy-utility trade-offs must be considered. Therefore, we propose a new scheme called search efficient privacy-preserving location-sharing (SELS) system. In our scheme, we create a new approach named associated grids to improve the efficiency of location-sharing systems while maintaining users’ privacy. In addition, by setting the user-defined access control policy proposed in our scheme, users’ flexible privacy-preserving requirements can be satisfied. Detailed complexity and security analysis show that the proposed scheme is a practical and efficient privacy-preserving solution. Extensive simulations are performed to validate the effectiveness and performance of our scheme.

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5

Perusco, Laura, and Katina Michael. "Control, trust, privacy, and security: evaluating location-based services." IEEE Technology and Society Magazine 26, no. 1 (2007): 4–16. http://dx.doi.org/10.1109/mtas.2007.335564.

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6

Luceri, Luca, Davide Andreoletti, Massimo Tornatore, Torsten Braun, and Silvia Giordano. "Measurement and control of geo-location privacy on Twitter." Online Social Networks and Media 17 (May 2020): 100078. http://dx.doi.org/10.1016/j.osnem.2020.100078.

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7

Chu, Xiang, Jun Liu, Daqing Gong, and Rui Wang. "Preserving Location Privacy in Spatial Crowdsourcing Under Quality Control." IEEE Access 7 (2019): 155851–59. http://dx.doi.org/10.1109/access.2019.2949409.

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8

Qi He, Dapeng Wu, and P. Khosla. "The quest for personal control over mobile location privacy." IEEE Communications Magazine 42, no. 5 (2004): 130–36. http://dx.doi.org/10.1109/mcom.2004.1299356.

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9

Góes, Rômulo Meira, Blake C. Rawlings, Nicholas Recker, Gregory Willett, and Stéphane Lafortune. "Demonstration of Indoor Location Privacy Enforcement using Obfuscation." IFAC-PapersOnLine 51, no. 7 (2018): 145–51. http://dx.doi.org/10.1016/j.ifacol.2018.06.293.

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10

Xu, Chuan, Li Luo, Yingyi Ding, Guofeng Zhao, and Shui Yu. "Personalized Location Privacy Protection for Location-Based Services in Vehicular Networks." IEEE Wireless Communications Letters 9, no. 10 (2020): 1633–37. http://dx.doi.org/10.1109/lwc.2020.2999524.

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