Zhiwei Si
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Zhiwei Si has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
- Jingcheng Song
Linyi University
2 shared publications - Hu Xiong
University of Electronic Science and Technology of China
1 shared publication - Jingxue Chen
College of Cryptology and Cyber Science, Nankai University, Tianjin, China
1 shared publication - Juhao Wang
School of Information Science and Engineering, Linyi University, Linyi, China
1 shared publication - Manoranjan Mohanty
Carnegie Mellon University in Qatar
1 shared publication - Pengbiao Zhao
University of Electronic Science and Technology of China
1 shared publication
Latest Publications
- 28 January 2025
- Published by Springer Nature
- p. 125-139
GDCTSA: A Grouped Data Collection Scheme for Trustworthy Systems Assessment
- 28 January 2025
- Published by Springer Nature
- p. 125-139
GDCTSA: A Grouped Data Collection Scheme for Trustworthy Systems Assessment
- 13 December 2024
- Published by Springer Nature
- p. 239-253
UFL: Unlinkable Federated Learning Through Shuffle and Shamir’s Secret Sharing
- 13 December 2024
- Published by Springer Nature
- p. 239-253
UFL: Unlinkable Federated Learning Through Shuffle and Shamir’s Secret Sharing
- Open Access
EADC: An Efficient Anonymous Data Collection Scheme with Blockchain in Internet of Things
The integration of smart contracts (SCs) within blockchain technology represents a pivotal direction in the evolution of the Internet of Things (IoT), enabling decentralization and enhancing user trust in the system. However, ensuring data privacy is a fundamental challenge that must be addressed during the deployment of these SCs. Many scholars have adopted data aggregation to protect privacy, but these methods are difficult to achieve fine-grained data collection. To this end, this paper proposes an efficient anonymous data collection (EADC) scheme suitable for the IoT environment. The scheme combines matrix algorithms with homomorphic encryption (HE) technology to effectively cut off the connection between users and data during data upload. In addition, the EADC scheme introduces a sophisticated data grouping protocol to improve the overall efficiency of the system. Analysis shows that the scheme can achieve efficient data collection without compromising user privacy.
Open AccessEADC: An Efficient Anonymous Data Collection Scheme with Blockchain in Internet of Things
The integration of smart contracts (SCs) within blockchain technology represents a pivotal direction in the evolution of the Internet of Things (IoT), enabling decentralization and enhancing user trust in the system. However, ensuring data privacy is a fundamental challenge that must be addressed during the deployment of these SCs. Many scholars have adopted data aggregation to protect privacy, but these methods are difficult to achieve fine-grained data collection. To this end, this paper proposes an efficient anonymous data collection (EADC) scheme suitable for the IoT environment. The scheme combines matrix algorithms with homomorphic encryption (HE) technology to effectively cut off the connection between users and data during data upload. In addition, the EADC scheme introduces a sophisticated data grouping protocol to improve the overall efficiency of the system. Analysis shows that the scheme can achieve efficient data collection without compromising user privacy.