Threshold Signatures With Verifiably Timed Combining and Message-Dependent TracingShow others and affiliations
2025 (English)In: IEEE Transactions on Information Forensics and Security, ISSN 1556-6013, E-ISSN 1556-6021, Vol. 20, p. 9477-9491Article in journal (Refereed) Published
Abstract [en]
Threshold Signature (TS) is one of the fundamental cryptographic primitives adopted in many practical applications. Current Threshold, Accountable, and Private Signature (TAPS) schemes suffer from delayed combining, unverifiable combining, and message-independent tracing. More precisely, a malicious combiner may delay the combination of signature shares and replace some signature shares from honest signers with ones from colluding signers, and an unrestricted tracer can reveal signers' identities arbitrarily. In this work, we introduce a new scheme called TiMTAPS under a stronger security model. First, we sew homomorphic time-lock puzzles into the Schnorr signature, allowing puzzles to be combined and opened as needed. Second, we knit the Schnorr signature with homomorphic commitment for verifiable combining. Third, we infuse the combining phase with an identity-based key encapsulation mechanism for message-dependent tracing. Next, formalize the definitions and requirements for TiMTAPS. Then, we present a concrete construction and formally prove its privacy and security. We build a prototype of TiMTAPS based on Ethereum. Results from extensive experiments exhibit its practicability and efficiency, e.g., combining (tracking) 10 signature sets with a threshold value of 5 requires only 3.72 s (12.44 s) for the threshold signature.
Place, publisher, year, edition, pages
IEEE, 2025. Vol. 20, p. 9477-9491
Keywords [en]
Threshold signature, security, privacy, timed cryptography, commitment, key encapsulation
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-124116DOI: 10.1109/TIFS.2025.3607250ISI: 001574179600007OAI: oai:DiVA.org:oru-124116DiVA, id: diva2:2004317
Note
This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 62372149, Grant U23A20303, and Grant 62572168; in part by the Anhui Provincial Natural Science Foundation under Grant 2508085MF151; in part by the Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), unde rGrant BigKEOpen2025-04.
2025-10-072025-10-072025-10-07Bibliographically approved