Secure Multi-Party Computation

Benefits & Challenges

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References

Note: The sources marked in purple are ideal for further reading, particularly for a non-technical audience. Tester
Y. Lindell. Secure Multiparty Computation (MPC). Cryptology ePrint Archive, Paper 2020/300. 2020. doi: 10.1145/3387108.
D. Huynh. Homomorphic Encryption intro: Part 1: Overview and use cases. 2020.
D. Evans, V. Kolesnikov, and M. Rosulek. A Pragmatic Introduction to Secure MultiParty Computation. Version: April 15, 2020. Now Publishers, Inc., 2018.
C. Zhao, S. Zhao, M. Zhao, Z. Chen, C.-Z. Gao, H. Li, and Y.-a. Tan. “Secure multi-party computation: theory, practice and applications”. In: Information Sciences 476 (2019), pp. 357–372. doi: 10.1016/j.ins.2018.10.024.
Nawazmalla. What is Secure Multiparty Computation? (visited on 06/04/2023).
N. Kaaniche, M. Laurent, and S. Belguith. “Privacy enhancing technologies for solving the privacy-personalization paradox: Taxonomy and survey”. In: Journal of Network and Computer Applications 171 (2020), p. 102807. doi: 10.1016/j.jnca.2020.102807.
Keyless Technologies. A beginner’s guide to Secure Multiparty Computation. 2020. (visited on 06/04/2023).

References

Note: The sources marked in purple are ideal for further reading, particularly for a non-technical audience. Tester
Chainlink. Secure Multi-Party Computation. 2023. (visited on 06/04/2023).
IEEE Digital Privacy. Applications of Multiparty Computation. (visited on 06/04/2023).
Y. Yang, X. Huang, X. Liu, H. Cheng, J. Weng, X. Luo, and V. Chang. “A comprehensive survey on secure outsourced computation and its applications”. In: IEEE Access 7 (2019), pp. 159426–159465. doi: 10.1109/ ACCESS.2019.2949782.
J. Bringer, H. Chabanne, and A. Patey. “Privacy-preserving biometric identification using secure multiparty computation: An overview and recent trends”. In: IEEE Signal Processing Magazine 30.2 (2013), pp. 42–52. doi: 10.1109/MSP.2012.2230218.
M. Hastings, B. Hemenway, D. Noble, and S. Zdancewic. “Sok: General purpose compilers for secure multi-party computation”. In: 2019 IEEE symposium on security and privacy (SP). IEEE. 2019, pp. 1220–1237. doi: 10.1109/ SP.2019.00028.
N. Volgushev, M. Schwarzkopf, B. Getchell, M. Varia, A. Lapets, and A. Bestavros. “Conclave: secure multi-party computation on big data”. In: Proceedings of the Fourteenth EuroSys Conference 2019. EuroSys ’19. 2019, pp. 1– 18. doi: 10.1145/3302424.3303982.
F. N. Wirth, T. Kussel, A. Müller, K. Hamacher, and F. Prasser. “EasySMPC: a simple but powerful no-code tool for practical secure multiparty computation”. In: BMC bioinformatics 23.1 (2022), p. 531. doi: 10.1186/ s12859-022-05044-8.

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