MPC-ABC: Blockchain-Based Network Communication for Efficiently Secure Multiparty Computation-Reference-Cited by-同舟云学术

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MPC-ABC: Blockchain-Based Network Communication for Efficiently Secure Multiparty Computation

Published:2023-07-21 Issue:4 Volume:31 Page:
ISSN:1064-7570
Container-title:Journal of Network and Systems Management
language:en
Short-container-title:J Netw Syst Manage

Author:

Bautista Oscar G.,Manshaei Mohammad Hossein,Hernandez Richard,Akkaya Kemal,Homsi Soamar,Uluagac Selcuk

Funder

National Science Foundation

Air Force Research Laboratory

Publisher

Springer Science and Business Media LLC

Subject

Strategy and Management,Computer Networks and Communications,Hardware and Architecture,Information Systems

Link

https://link.springer.com/content/pdf/10.1007/s10922-023-09739-y.pdf

Reference39 articles.

1. Damgård, I., Keller, M., Larraia, E., Pastro, V., Scholl, P., Smart, N.P.: Practical covertly secure mpc for dishonest majority–or: breaking the spdz limits. In: European Symposium on Research in Computer Security, pp. 1–18 (2013). Springer

2. Bogdanov, D., Talviste, R., Willemson, J.: Deploying secure multi-party computation for financial data analysis. In: International Conference on Financial Cryptography and Data Security, pp. 57–64 (2012). Springer

3. Damgård, I., Damgård, K., Nielsen, K., Nordholt, P.S., Toft, T.: Confidential benchmarking based on multiparty computation. In: International Conference on Financial Cryptography and Data Security, pp. 169–187 (2016). Springer

4. Li, D., Liao, X., Xiang, T., Wu, J., Le, J.: Privacy-preserving self-serviced medical diagnosis scheme based on secure multi-party computation. Comput. Secur. 90, 101701 (2020)

5. Wagh, S., Gupta, D., Chandran, N.: SecureNN: 3-party secure computation for neural network training. Proc. Privacy Enhancing Technol. 2019(3), 26–49 (2019). https://doi.org/10.2478/popets-2019-0035

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1. Blockchain-assisted Verifiable Secure Multi-Party Data Computing;Computer Networks;2024-11

2. SMPTC3: Secure Multi-Party Protocol Based Trusted Cross-Chain Contracts;Mathematics;2024-08-19

3. Optimizing the Parameters of Pipelined Multi-Party Computation for Privacy-Preserving Machine Learning Applications;ICC 2024 - IEEE International Conference on Communications;2024-06-09

4. Privacy-Preserving Oriented Design for Multi-Modality Models using FL;2023 5th IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA);2023-11-01

5. Privacy-Preserving Collision Detection for Drone-based Aerial Package Delivery using Secure Multi-Party Computation;Proceedings of the Twenty-fourth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing;2023-10-16

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