arXiv subjects

Leo Kao

Publications and source records attributed to Leo Kao.

4 recordsLinked to original sources

arXiv2026

TALUS: FIPS-204-Exact Threshold ML-DSA via Boundary Clearance

We present TALUS, a threshold signing protocol for ML-DSA (FIPS 204) that supports an arbitrary number of parties, emits signatures accepted by any unmodified ML-DSA verifier, and reduces, for a bounded number of signatures per key, to ML-DSA's own assumptions (Module-LWE and SelfTargetMSIS). Its core is the Boundary Clearance Condition (BCC): for a constant fraction of nonces (31.7% at ML-DSA-65), the secret vector s2 provably cannot cross a rounding boundary, so the s2-dependent rejection check can be enforced offline on preprocessed nonces. This removes the interaction that forces multi-round signing in prior schemes, yielding a TEE-assisted one-round profile and a fully distributed honest-majority MPC profile with two online rounds, both built on a Carry Elimination Framework that computes the shared commitment w1 = HighBits(Ay) on secret shares, a step we show is unavoidable. We prove a lower bound: any FIPS-204-exact threshold scheme revealing a summed (Irwin-Hall) nonce admits an efficient key-recovery attack after about 2^30 signatures under one key (at ML-DSA-65), so no q_s-independent unforgeability is possible for this class. Complementing it, a q_s-bounded analysis certifies over 105 bits at the operational signing cap for the external view, with insider ceilings disclosed per view and a mandatory key rotation capping each key's signing lifetime. A Rust implementation across all three FIPS 204 levels demonstrates practicality.

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arXiv2026

FIPS 204-Compatible Threshold ML-DSA via Shamir Nonce DKG

We present the first threshold ML-DSA (FIPS 204) scheme achieving nonce share privacy (conditional min-entropy guarantee; no computational assumptions) with arbitrary thresholds, while producing standard 3.3 KB signatures verifiable by unmodified implementations. Our primary technique, Shamir nonce DKG, generates the signing nonce as a degree-$(T-1)$ Shamir sharing, matching the structure of the long-term secret. This gives each honest party's nonce share conditional min-entropy exceeding $5\times$ the secret-key entropy for signing sets of size at most 17. In coordinator-based profiles (P1, P3+), this removes the two-honest requirement ($|S| \geq T$ suffices); in the fully distributed profile (P2), mask-hiding additionally requires $|S \setminus C| \geq 2$. Key privacy of the aggregate signature is an open problem, analogous to single-signer ML-DSA. As a secondary technique, pairwise-canceling PRF masks handle three challenges unique to lattice-based threshold signing: commitment binding, the r0-check predicate, and response aggregation.

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arXiv2025

Post-Quantum-Resilient Audit Evidence for Long-Lived Regulated Systems: Security Models, Migration Patterns, and Case Study

Constant-size cryptographic evidence records are increasingly used to build audit trails for regulated AI workloads in clinical, pharmaceutical, and financial settings, where each execution is summarized by a compact, verifiable record of code identity, model version, data digests, and platform measurements. Existing instantiations, however, typically rely on classical signature schemes whose long-term security is threatened by quantum-capable adversaries. In this paper we formalize security notions for evidence structures in the presence of quantum adversaries and study post-quantum (PQ) instantiations and migration strategies for deployed audit logs. We recall an abstraction of constant-size evidence structures and introduce game-based definitions of Q-Audit Integrity, Q-Non-Equivocation, and Q-Binding, capturing the inability of a quantum adversary to forge, equivocate, or rebind evidence items. We then analyze a hash-and-sign instantiation in the quantum random-oracle model (QROM), assuming an existentially unforgeable PQ signature scheme against quantum adversaries, and show that the resulting evidence structure satisfies these notions under standard assumptions. Building on this, we present three migration patterns for existing evidence logs: hybrid signatures, re-signing of legacy evidence, and Merkle-root anchoring, and analyze their security, storage, and computational trade-offs. A case study based on an industrial constant-size evidence platform for regulated AI at Codebat Technologies Inc. suggests that quantum-safe audit trails are achievable with moderate overhead and that systematic migration can significantly extend the evidentiary lifetime of existing deployments.

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arXiv2025

Constant-Size Cryptographic Evidence Structures for Regulated AI Workflows

Regulated AI workflows (such as clinical trials, medical decision support, and financial compliance) must satisfy strict auditability and integrity requirements. Existing audit-trail mechanisms rely on variable-length records, bulky cryptographic transcripts, or ad-hoc schemas, suffering from metadata leakage, irregular performance, and weak alignment with formal security notions.This paper introduces constant-size cryptographic evidence structures, a general abstraction for verifiable audit evidence in regulated AI workflows. Each evidence item is a fixed-size tuple of cryptographic fields designed to (i) bind strongly to workflow events and configurations, (ii) support constant-size storage and uniform verification cost per event, and (iii) compose cleanly with hash-chain and Merkle-based audit constructions. We formalize a model of regulated AI workflows, define syntax and algorithms for evidence structures, and prove security properties (evidence binding, tamper detection, and non-equivocation) via game-based definitions under standard assumptions (collision-resistant hashing and EUF-CMA signatures).We present a generic hash-and-sign construction using a collision-resistant hash function and a standard signature scheme, and show how to integrate it with hash-chained logs, Merkle-tree anchoring, and trusted execution environments. We implement a prototype library and report microbenchmarks on commodity hardware, demonstrating that per-event overhead is small and predictable. This work aims to provide a foundation for standardized audit mechanisms in regulated AI, with implications for clinical trial management, pharmaceutical compliance, and medical AI governance.

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