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SILMARILS-MV offers dealer-free signatures for permissioned blockchains

Original title: SILMARILS-MV: Information-Theoretically Secure Multi-Verifier Signatures with Dealer-Free Key Issuance and Linear Aggregation for Permissioned Blockchains

arXiv cs.CRby Khadijeh Bagheri ()1 min read
AI summary · x-ai/grok-4.7Summaries by Vani AI

Threshold notarization keeps certificates at 32(ω+1) bytes and cuts authentication traffic 22x versus ML-DSA-44 at 1,000 validators.

SILMARILS-MV is an information-theoretic multi-verifier signature for permissioned blockchains that avoids a trusted dealer, quantum channels, and single-use keys. Validators issue keys with perfectly secure MPC when t < n/3, and threshold notarization yields constant-size finality certificates. Against unbounded adversaries corrupting up to ω verifiers, an honest verifier accepts a forgery with probability at most Q/q + 1/(q−1). A Rust HotStuff prototype with 100 validators commits 2.8x more transactions per second than ML-DSA-44 with 67x less cryptographic CPU.

Unconfirmed: Performance figures come from the authors' analytic model and prototype, not an independent deployment.

Key facts
  • Certificates verify in 2.4 microseconds.
  • Arbiter-verified keys give clients 32-byte signatures.
  • At 1,000 validators, authentication traffic is 71 KiB per validator per block.
  • Monte-Carlo attacks match the stated forgery bound.
  • Share refresh and retroactive verifier enrollment are supported.
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From the original article

1 min read at arxiv.org

Permissioned blockchains authenticate transactions, votes and quorum certificates with computationally secure signatures, and post-quantum replacements inflate consensus traffic by orders of magnitude. Information-theoretic alternatives have required a trusted authority, quantum channels, per-signature interaction, or single-use keys with bounded transfer.

arXiv cs.CR

By Khadijeh Bagheri · arxiv.org

Read the full article at arXiv cs.CR

A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.