Citation

Chin, Ji Jian and Tan, Syh Yuan and Yip, Sook Chin and Suhaimi, Nur Hanis Sabrina (2025) A Technical Review of FROST Threshold Signatures: Variants, Extensions, Security, and RFC9591. In: 3rd International Conference on Cyber Resilience, ICCR 2025, July 2025 - 4 July 2025, Dubai.

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Abstract

Threshold signature schemes enable a group of participants to collaboratively generate a signature if a subset of t out of n participants willingly participate in the signing process. Among recent developments, FROST (Flexible RoundOptimised Schnorr Threshold signatures) stands out for its efficiency, flexibility, and strong security guarantees in both theoretical and applied settings. This survey provides a comprehensive overview of FROST, starting from its cryptographic foundations in Schnorr signatures and the discrete logarithm problem to its formal security models and threat assumptions, including the unique robustness properties of FROST in the presence of malicious or aborting signers. We examine its tworound signing protocol, distributed key generation (DKG), and integration with secure multiparty computation frameworks. Furthermore, the survey categorises and analyses key extensions and adaptations of FROST, such as proactive and dynamic group configurations, post-quantum variants, compatibility with verifi- able secret sharing, and deployments over alternative curves and signature primitives. We also evaluate practical implementations, standardisation efforts (e.g., by the IRTF CFRG), and real-world applications in blockchain and distributed systems. By mapping out current research and highlighting open challenges, this survey aims to guide future work in robust, scalable threshold signature protocols anchored in the FROST paradigm

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Threshold signature, survey
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST) Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	17 Mar 2026 03:01
Last Modified:	17 Mar 2026 03:01
URII:	http://shdl.mmu.edu.my/id/eprint/15470

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