Tao Wang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Tao Wang has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Lu Zhou
Nanjing University of Aeronautics and Astronautics
1 shared publication - Zhaoyang Han
Nanjing University of Aeronautics and Astronautics
1 shared publication - Zhusen Liu
Hangzhou Innovation Institute of Beihang University
1 shared publication
Latest Publications
- Open Access
- 30 November 2023
- Published by MDPI AG in Electronics
- Vol. 12 (23) , 4840
Efficient Decision-Making Scheme Using Secure Multiparty Computation with Correctness Validation
In the era of big data, it is essential to securely and efficiently combine the large amounts of private data owned by different companies or organizations to make correct decisions. Secure Multiparty Computation (SMPC) works as a general cryptographic primitive, which enables distributed parties to collaboratively compute an arbitrary functionality without revealing their own private inputs. While SMPC may potentially address this task, several issues, such as computation efficiency and correctness validation, have to be overcome for practical realizations. To tackle these issues, we designed a secure and efficient decision-making scheme to enable clients to outsource data and computations to cloud servers while ensuring the integrity and confidentiality of the input and output, in addition to the correctness of the results. Moreover, we implemented our scheme based on an SMPC computation framework named MP-SPDZ. The experimental evaluation results showed that our proposed scheme is feasible and efficient for practical realizations.
- 30 November 2023
- Published by MDPI AG in Electronics
- Vol. 12 (23) , 4840
Open AccessEfficient Decision-Making Scheme Using Secure Multiparty Computation with Correctness Validation
In the era of big data, it is essential to securely and efficiently combine the large amounts of private data owned by different companies or organizations to make correct decisions. Secure Multiparty Computation (SMPC) works as a general cryptographic primitive, which enables distributed parties to collaboratively compute an arbitrary functionality without revealing their own private inputs. While SMPC may potentially address this task, several issues, such as computation efficiency and correctness validation, have to be overcome for practical realizations. To tackle these issues, we designed a secure and efficient decision-making scheme to enable clients to outsource data and computations to cloud servers while ensuring the integrity and confidentiality of the input and output, in addition to the correctness of the results. Moreover, we implemented our scheme based on an SMPC computation framework named MP-SPDZ. The experimental evaluation results showed that our proposed scheme is feasible and efficient for practical realizations.