Secure Multi-Party Computation Protocol Based on Trusted Execution Environment

doi:10.3969/j.issn.1671-1122.2025.09.011

Abstract

Abstract:

As data becomes increasingly valuable in information systems, privacy protection must be addressed alongside data utilization. Secure multi-party computation enables collaborative computation without direct data sharing between parties, serving as a crucial technology for privacy-preserving. Traditional multi-party computation relies on complex cryptographic protocols, incurring high communication and computational overheads that hinder practical deployment. This paper proposed an outsourced multi-party computation protocol based on the native security mechanisms of trusted execution environment. It not only ensures security properties such as privacy, correctness, and input independence, but also achieves high efficiency and scalability. The proposed protocol offers a new technical path for secure and practical multi-party computation systems, and lowers deployment barriers. It provides the practical meaning for the privacy computing.

Key words: privacy computing, trusted execution environment, secure multi-party computation

CLC Number:

TP309

SHI Yijuan, ZHOU Danping, FAN Lei, LIU Yin. Secure Multi-Party Computation Protocol Based on Trusted Execution Environment[J]. Netinfo Security, 2025, 25(9): 1439-1446.

Figures/Tables 3

References 22

[1]	FENG Dengguo, ZHANG Min, LI Hao. Big Data Security and Privacy Protection[J]. Chinese Journal of Computers, 2014, 37(1): 246-258.
	冯登国, 张敏, 李昊. 大数据安全与隐私保护[J]. 计算机学报, 2014, 37(1): 246-258.
[2]	TANG Chunming, LIN Xuhui. Protocol of Privacy-Preserving Set Intersection Computation[J]. Netinfo Security, 2020, 20(1): 9-15.
	唐春明, 林旭慧. 隐私保护集合交集计算协议[J]. 信息网络安全, 2020, 20(1): 9-15.
[3]	FENG Dengguo, YANG Kang. Concretely Efficient Secure Multi-Party Computation Protocols: Survey and More[EB/OL]. (2022-06-14)[2025-06-02]. https://doaj.org/article/5cca8bd884a04430a75a15f52e2edd5c.
[4]	AGRAWAL N, SHAHIN S A, KUSNER M J, et al. QUOTIENT: Two-Party Secure Neural Network Training and Prediction[C]// ACM. 2019 ACM SIGSAC Conference on Computer and Communications Security. New York: ACM, 2019: 1231-1247.
[5]	GARG S, JAIN A, MUKHERJEE P, et al. Scalable Multiparty Computation from Non-Linear Secret Sharing[C]// Springer. CRYPTO 2024. Heidelberg: Springer, 2024: 384-417.
[6]	YAO A C. Protocols for Secure Computations[C]// IEEE. 23rd Annual Symposium on Foundations of Computer Science (SFCS 1982). New York: IEEE, 1982: 160-164.
[7]	YAO A C. How to Generate and Exchange Secrets[C]// IEEE. 27th Annual Symposium on Foundations of Computer Science (SFCS 1986). New York: IEEE, 1986: 162-167.
[8]	EVANS D, KOLESNIKOV V, ROSULEK M. A Pragmatic Introduction to Secure Multi-Party Computation[M]. Boston: Now Foundations and Trends, 2018.
[9]	CHOI J I, BUTLER K R B. Secure Multiparty Computation and Trusted Hardware: Examining Adoption Challenges and Opportunities[J]. Security and Communication Networks, 2019, 64(5): 18-21.
[10]	SABT M, ACHEMLAL M, BOUABDALLAH A. Trusted Execution Environment: What It is, and What It is Not[C]// IEEE. 2015 IEEE Trustcom/BigDataSE/ISPA. New York: IEEE, 2015: 57-64.
[11]	KÜÇÜK K A, PAVERD A, MARTIN A, et al. Exploring the Use of Intel SGX for Secure Many-Party Applications[C]// ACM. The 1st Workshop on System Software for Trusted Execution. New York: ACM, 2016: 1-6.
[12]	ANATI I, GUERON S, JOHNSON S, et al. Innovative Technology for CPU Based Attestation and Sealing[C]// ACM. 2nd International Workshop on Hardware and Architectural Support for Security and Privacy. New York: ACM, 2013: 1-7.
[13]	PASS R, SHI E, TRAMÈR F. Formal Abstractions for Attested Execution Secure Processors[C]// Springer. EUROCRYPT 2017. Heidelberg: Springer, 2017: 260-289.
[14]	CANETTI R. Universally Composable Security: A New Paradigm for Cryptographic Protocols[C]// IEEE. The 42nd IEEE Symposium on Foundations of Computer Science. New York: IEEE, 2001: 136-145.
[15]	CHOUDHURI A R, GREEN M, JAIN A, et al. Fairness in an Unfair World: Fair Multiparty Computation from Public Bulletin Boards[C]// ACM. 2017 ACM SIGSAC Conference on Computer and Communications Security. New York: ACM, 2017: 719-728.
[16]	GARFINKEL T, PFAFF B, CHOW J, et al. Terra: A Virtual Machine-Based Platform for Trusted Computing[C]// ACM. The Nineteenth ACM Symposium on Operating Systems Principles. New York: ACM, 2003: 193-206.
[17]	VASUDEVAN A, OWUSU E, ZHOU Zongwei, et al. Trustworthy Execution on Mobile Devices: What Security Properties Can My Mobile Platform Give Me[C]// Springer. Trust and Trustworthy Computing. Heidelberg: Springer, 2012: 159-178.
[18]	GlobalPlatform. TEE System Architecture[EB/OL]. (2022-05-16)[2025-06-02]. https://globalplatform.org/wp-content/uploads/2022/05/GPD_SPE_009-GPD_TEE_SystemArchitecture_v1.3_PublicRelease_signed.pdf.
[19]	LI Xiaoguo, ZHAO Bowen, YANG Guomin, et al. A Survey of Secure Computation Using Trusted Execution Environments[EB/OL]. (2023-02-23)[2025-06-02]. https://doi.org/10.48550/arXiv.2302.12150.
[20]	ARBAUGH W A, FARBER D J, SMITH J M. A Secure and Reliable Bootstrap Architecture[C]// IEEE. 1997 IEEE Symposium on Security and Privacy. New York: IEEE, 1997: 65-71.
[21]	BARTUSEK J, BERGAMASCHI T, KHOURY S, et al. On the Communication Complexity of Secure Multi-Party Computation with Aborts[C]// ACM. The 43rd ACM Symposium on Principles of Distributed Computing. New York: ACM, 2024: 480-491.
[22]	NOAH A, OKUNOLA A. A Comparative Analysis of Homomorphic Encryption and Secure Multi-Party Computation for Preserving Data Privacy in Cloud-Based Financial Services Authors[EB/OL]. (2025-05-28)[2025-06-02]. https://www.researchgate.net/publication/392165415_A_Comparative_Analysis_of_Homomorphic_Encryption_and_Secure_Multi-Party_Computation_for_Preserving_Data_Privacy_in_Cloud-Based_Financial_Services.