List of Titles

A data reporting protocol with revocable anonymous authentication for edge-assisted intelligent transport systems

- Wang, Yanping, Wang, Xiaofen, Dai, Hong-Ning, Zhang, Xiaosong, Imran, Muhammad

Authors: Wang, Yanping , Wang, Xiaofen , Dai, Hong-Ning , Zhang, Xiaosong , Imran, Muhammad
Date: 2023
Type: Text , Journal article
Relation: IEEE Transactions on Industrial Informatics Vol. 19, no. 6 (2023), p. 7835-7847
Full Text: false
Reviewed:
Description: Intelligent Transport Systems (ITS) have received growing attention recently driven by technical advances in Industrial Internet of Vehicles (IIoV). In IIoV, vehicles report traffic data to management infrastructures to achieve better ITS services. To ensure security and privacy, many anonymous authentication-enabled data reporting protocols are proposed. However, these protocols usually require a large number of preloaded pseudonyms or involve a costly and irrevocable group signature. Thus, they are not ready for realistic deployment due to large storage overhead, expensive computation costs, or absence of malicious users' revocation. To address these issues, we present a novel data reporting protocol for edge-assisted ITS in this paper, where the traffic data is sent to distributed edge nodes for local processing. Specifically, we propose a new anonymous authentication scheme fine-tuned to fulfill the needs of vehicular data reporting, which allows authenticated vehicles to report unlimited unlinkable messages to edge nodes without huge pseudonyms download and storage costs. Moreover, we designed an efficient certificate update scheme based on a bivariate polynomial function. In this way, malicious vehicles can be revoked with time complexity O(1). The security analysis demonstrates that our protocol satisfies source authentication, anonymity, unlinkability, traceability, revocability, nonframeability, and nonrepudiation. Further, extensive simulation results show that the performance of our protocol is greatly improved since the signature size is reduced by at least 8%, the computation costs in message signing and verification are reduced by at least 56% and 67%, respectively, and the packet loss rate is reduced by at least 14%. © 2005-2012 IEEE.

PyHENet : a generic framework for privacy-preserving DL inference based on fully homomorphic encryption

- Chen, Qian, Yao, Lin, Wu, Yulin, Wang, Xuan, Zhang, Weizhe, Jiang, Zoe, Liu, Yang, Alazab, Mamoun

Authors: Chen, Qian , Yao, Lin , Wu, Yulin , Wang, Xuan , Zhang, Weizhe , Jiang, Zoe , Liu, Yang , Alazab, Mamoun
Date: 2022
Type: Text , Conference paper
Relation: 4th International Conference on Data Intelligence and Security, ICDIS 2022, Shenzhen, China, 24-26 August 2022, Proceedings 2022 4th International Conference on Data Intelligence and Security ICDIS 2022 p. 127-133
Full Text: false
Reviewed:
Description: Deep learning inference provides inference service by service provider with model for client with input of personal data. Due to the huge commercial value inside, on one hand, both client's original data and inference output should be kept secret from others, even including service provider. On the other hand, service provider's model should be kept secret, especially from his competitor. Current research on privacy-preserving deep learning inference focuses on building models with specific data. This paper proposes a generic framework PyHENet of privacy-preserving deep learning inference based on Pytorch and lattice-based FHE, such that crypto library can be flexibly embedded into network. Firstly, raw data is encrypted by lattice-based FHE and uploaded to service provider. Secondly, convolutional computation over float-point ciphertext data is proposed for service provider to execute low accuracy loss inference with aided parallel method SIMD. Thirdly, inference result in ciphertext format is sent back to client for decryption. To improve efficiency, inference procedure can be further divided into two phases. All the computations during the second phase are in plaintext format with GPU acceleration, while keeping the first phase unchanged. Using the same model and parameters, the relative accuracy of PyHENet is almost 100% compared to the plaintext inference. This paper is the first to propose a general framework of neural networks for fully homomorphic cryptographic inference, and is based on mainstream deep learning frameworks, which is both secure and more conducive to development. © 2022 IEEE.

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Marking the pace : a blockchain-enhanced privacy-traceable strategy for federated recommender systems

- Cai, Zhen, Tang, Tao, Yu, Shuo, Xiao, Yunpeng, Xia, Feng

Authors: Cai, Zhen , Tang, Tao , Yu, Shuo , Xiao, Yunpeng , Xia, Feng
Date: 2024
Type: Text , Journal article
Relation: IEEE Internet of Things Journal Vol. 11, no. 6 (2024), p. 10384-10397
Full Text:
Reviewed:
Description: Federated recommender systems have been crucially enhanced through data sharing and continuous model updates, attributed to the pervasive connectivity and distributed computing capabilities of Internet of Things (IoT) devices. Given the sensitivity of IoT data, transparent data processing in data sharing and model updates is paramount. However, existing methods fall short in tracing the flow of shared data and the evolution of model updates. Consequently, data sharing is vulnerable to exploitation by malicious entities, raising significant data privacy concerns, while excluding data sharing will result in suboptimal recommendations. To mitigate these concerns, we present LIBERATE, a privacy-traceable federated recommender system. We design a blockchain-based traceability mechanism, ensuring data privacy during data sharing and model updates. We further enhance privacy protection by incorporating local differential privacy in user-server communication. Extensive evaluations with the real-world data set corroborate LIBERATE's capabilities in ensuring data privacy during data sharing and model update while maintaining efficiency and performance. Results underscore blockchain-based traceability mechanism as a promising solution for privacy preserving in federated recommender systems. © 2014 IEEE.

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