Secure Multiparty Computation and Secret Sharing
Abstract
In a data-driven society, individuals and companies encounter numerous situations where private information is an important resource. How can parties handle confidential data if they do not trust everyone involved? This text is the first to present a comprehensive treatment of unconditionally secure techniques for multiparty computation (MPC) and secret sharing. In a secure MPC, each party possesses some private data, while secret sharing provides a way for one party to spread information on a secret such that all parties together hold full information, yet no single party has all the information. The authors present basic feasibility results from the last 30 years, generalizations to arbitrary access structures using linear secret sharing, some recent techniques for efficiency improvements, and a general treatment of the theory of secret sharing, focusing on asymptotic results with interesting applications related to MPC.
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Secure multi-party computation (SMPC) splits data or program states between N parties using secret sharing.
“Secure multi-party computation (SMPC) [26] splits data or program states (e.g., states of blockchain's smart contract) between N parties using secret sharing.”
From Privacy-Preserving Solutions for Blockchain: Review and Challenges (2019) · cites this paper as [26]
SMPC requires the majority of participants to be honest.
“This method requires the majority of participants to be honest.”
From Privacy-Preserving Solutions for Blockchain: Review and Challenges (2019) · cites this paper as [26]
Only the work proposed by [146] was intended to deal with privacy aspects based on the use of blockchain in smart cities.
“Based on it, only the work proposed by [146] was intended to deal with privacy aspects based on the use of blockchain.”
From Privacy-Preserving Solutions for Blockchain: Review and Challenges (2019) · cites this paper as [41]
Secure multiparty computation is another approach for privacy-preserving with a cryptographic framework.
“Secure multiparty computation (SMPC) is another approach for privacy-preserving with a cryptographic framework [159].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [159]
Cites
Shamir, A. (1979) · Communications of the ACM · doi:10.1145/359168.359176 · Cited by 13,756 · see citing works
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Privacy-Preserving Solutions for Blockchain: Review and Challenges
Bernal Bernabe, J., Canovas, J. L., Hernandez-Ramos, J. L., Torres Moreno, R., & Skarmeta, A. (2019) · IEEE Access · doi:10.1109/access.2019.2950872 · Cited by 430 · see citing works
PPFA: Privacy Preserving Fog-Enabled Aggregation in Smart Grid
Lyu, L., Nandakumar, K., Rubinstein, B., Jin, J., Bedo, J., & Palaniswami, M. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2803782 · Cited by 240 · see citing works
Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis
Liu, J., Cen, X., Yi, C., Wang, F.-A., Ding, J., Cheng, J., Wu, Q., Gai, B., Zhou, Y., He, R., et al. (2025) · Genomics, Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzaf011 · Cited by 75 · see citing works
MNSSp3: medical big data privacy protection platform based on internet of things
Wu, X., Zhang, Y., Wang, A., Shi, M., Wang, H., & Liu, L. (2022) · Neural Computing and Applications · doi:10.1007/s00521-020-04873-z · Cited by 38 · see citing works
- Authors
- Cramer, R., Damgård, I. B., & Nielsen, J. B.
- Affiliations
- Leiden University (NL)
- Aarhus University (DK)
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2015-06-30
- OpenAlex
- W2397857137
- Cited by
- 595
- References
- 1
- Citations by year
- 2026: 34 · 2025: 84 · 2024: 66 · 2023: 70 · 2022: 70 · 2021: 72 · 2020: 60 · 2019: 49 · 2018: 48 · 2017: 17 · 2016: 16 · 2015: 8 · 2014: 1
- FWCI
- 17.18
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Cambridge University Press eBooks (publishedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands (submittedVersion)
- Cambridge University Press eBooks (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Physical Sciences › Computer Science › Artificial Intelligence › Coding theory and cryptography
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
- Keywords
- secure multi-party computation, secret sharing, linear secret sharing, access structures, unconditionally secure protocols, asymptotic analysis, private data protection
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