Proxy Re-Encryption-Based Traceability and Sharing Mechanism of the Power Material Data in Blockchain Environment

Song, Jien; Yang, Yang; Mei, Jie; Zhou, Gaofeng; Qiu, Weiqiang; Wang, Yixing; Li, Xu; Liu, Yanran; Jinyu, Jiang; Chu, Zhenyue; Tan, Weitao; Lin, Zhenzhi

doi:10.3390/en15072570

Cited by 8 publications

(3 citation statements)

References 30 publications

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“…In the literature, many recent studies are focusing on the privacy concern of data storing and sharing. Some of them use a blockchain-assisted method together with a proxy re-encryption (PRE) [7], [8], [9]. The main drawback of these studies is that in many PRE schemes, the proxy can easily determine the participants of the communication from the reencryption key.…”

Section: Related Workmentioning

confidence: 99%

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Decentralized Anonymous IoT Data Sharing with Key-Private Proxy Re-Encryption

Günsay,

Yayla

2024

International Journal of Information Security Science

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Secure and scalable data sharing is one of the main concerns of the Internet of Things (IoT) ecosystem. In this paper, we introduce a novel blockchain-based data-sharing construction designed to ensure full anonymity for both the users and the data. To share the encrypted IoT data stored on the cloud, users generate tokens, prove their ownership using zk-SNARKs, and target the destination address anonymously. To tackle the privacy concerns arising from uploading the data to the cloud, we use key-private re-encryption and share only the necessary information with the proxy. As the first time in the literature, we have integrated a token-based blockchain and a key private proxy re-encryption to achieve a fully anonymous data sharing scheme. Furthermore, we provide security proof of our proposed scheme is secure against existential forgery under chosen-plaintext attacks, under eDBDH assumption in the random oracle model.

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Section: Related Workmentioning

confidence: 99%

“…Recently, Song et al [8] adopted blockchainbased data traceability and sharing mechanism for the power material supply chain. They use proxy re-encryption to ensure security and privacy.…”

Section: Related Workmentioning

confidence: 99%

Decentralized Anonymous IoT Data Sharing with Key-Private Proxy Re-Encryption

Günsay,

Yayla

2024

International Journal of Information Security Science

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“…Fog computing can not only solve the problem of networked devices automation. More importantly, it requires less data transmission, which is conducive to improving local storage and computing capacity and eliminating the bottleneck of data storage and data transmission (Song et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Robot location privacy protection based on Q-learning particle swarm optimization algorithm in mobile crowdsensing

Kong

Chen

et al. 2022

Front. Neurorobot.

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In the recent years, with the rapid development of science and technology, robot location-based service (RLBS) has become the main application service on mobile intelligent devices. When people use location services, it generates a large amount of location data with real location information. If a malicious third party gets this location information, it will cause the risk of location-related privacy disclosure for users. The wide application of crowdsensing service has brought about the leakage of personal privacy. However, the existing privacy protection strategies cannot adapt to the crowdsensing environment. In this paper, we propose a novel location privacy protection based on the Q-learning particle swarm optimization algorithm in mobile crowdsensing. By generalizing tasks, this new algorithm makes the attacker unable to distinguish the specific tasks completed by users, cuts off the association between users and tasks, and protects users' location privacy. The strategy uses Q-learning to continuously combine different confounding tasks and train a confounding task scheme that can output the lowest rejection rate. The Q-learning method is improved by particle swarm optimization algorithm, which improves the optimization ability of the method. Experimental results show that this scheme has good performance in privacy budget error, availability, and cloud timeliness and greatly improves the security of user location data. In terms of inhibition ratio, the value is close to the optimal value.

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