IRDP Ride System: A Privacy Preservation System for Online Ride-Hailing

Zhang, Lei; Lin, Shi-Yi; Wang, Chao; Li, Jing; Liu, Yi; Sun, Yan

doi:10.1109/jiot.2022.3232504

IEEE Internet Things J.

2023

DOI: 10.1109/jiot.2022.3232504

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IRDP Ride System: A Privacy Preservation System for Online Ride-Hailing

Lei Zhang

Shi-Yi Lin

Chao Wang

et al.

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2024

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Cited by 2 publications

References 48 publications

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Design of an efficient dynamic context‐based privacy policy deployment model via dual bioinspired Q learning optimisations

Patel,

Jadhav

2024

IET Cyber-Phy Sys Theory & Ap

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A novel context‐based privacy policy deployment model enhanced with bioinspired Q‐learning optimisations is presented. The model addresses the challenge of maintaining privacy while ensuring data integrity and usability in various settings. Leveraging datasets including Adult (Census Income), Yelp, UC Irvine Machine Learning, and Movie Lens, the authors evaluate the model's performance against state‐of‐the‐art techniques, such as GEF AL, Deep Forest, and Robust Continual Learning. The approach employs Firefly Optimiser (FFO) and Ant Lion Optimiser (ALO) algorithms to dynamically adjust privacy parameters and handle large datasets efficiently. Additionally, Q‐learning enables intelligent decision‐making and rapid adaptation to changing data and network conditions and scenarios. Evaluation results demonstrate that the model consistently outperforms reference techniques across multiple metrics, including privacy levels, scalability, fidelity, and sensitivity management. By reducing reputational harm, minimising delays, and enhancing network quality, the model offers robust privacy protection without sacrificing data utility. Overall, a dynamic context‐based privacy policy deployment approach, enhanced with bioinspired Q‐learning optimisations, presents a significant advancement in privacy preservation methods. The combination of ALO, FFO, and Q‐learning techniques offers a practical solution to evolving data privacy challenges and enhances flexibility in various use case scenarios.

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Design of an efficient dynamic context‐based privacy policy deployment model via dual bioinspired Q learning optimisations

Patel,

Jadhav

2024

IET Cyber-Phy Sys Theory & Ap

View full text Add to dashboard Cite

show abstract

A location-based service scheme with attribute information privacy

Dai,

2024

PLoS ONE

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In location-based service (LBS), private information retrieval (PIR) is an efficient strategy used for preserving personal privacy. However, schemes with traditional strategy that constructed by information indexing are usually denounced by its processing time and ineffective in preserving the attribute privacy of the user. Thus, in order to cope with above two weaknesses, in this paper, based on the conception of ciphertext policy attribute-based encryption (CP-ABE), a PIR scheme based on CP-ABE is proposed for preserving the personal privacy in LBS (location privacy preservation scheme with CP-ABE based PIR, short for LPPCAP). In this scheme, query and feedback are encrypted with security two-parties calculation by the user and the LBS server, so as not to violate any personal privacy and decrease the processing time in encrypting the retrieved information. In addition, this scheme can also preserve the attribute privacy of users such as the query frequency as well as the moving manner. At last, we analyzed the availability and the privacy of the proposed scheme, and then several groups of comparison experiment are given, so that the effectiveness and the usability of proposed scheme can be verified theoretically, practically, and the quality of service is also preserved.

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IRDP Ride System: A Privacy Preservation System for Online Ride-Hailing

Cited by 2 publications

References 48 publications

Design of an efficient dynamic context‐based privacy policy deployment model via dual bioinspired Q learning optimisations

Design of an efficient dynamic context‐based privacy policy deployment model via dual bioinspired Q learning optimisations

A location-based service scheme with attribute information privacy

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