FIPS 204-Compatible Threshold ML-DSA via Masked Lagrange Reconstruction

• URL: http://arxiv.org/abs/2601.20917v1
• Date: Wed, 28 Jan 2026 18:13:47 GMT
• Title: FIPS 204-Compatible Threshold ML-DSA via Masked Lagrange Reconstruction
• Authors: Leo Kao,
• Abstract summary: Masked Lagrange reconstruction enables threshold ML-DSA with arbitrary thresholds $T$.<n>We produce standard 3.3 KB signatures verifiable by unmodified 204 implementations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present masked Lagrange reconstruction, a technique that enables threshold ML-DSA (FIPS 204) with arbitrary thresholds $T$ while producing standard 3.3 KB signatures verifiable by unmodified FIPS 204 implementations. Concurrent approaches have limitations: Bienstock et al. (ePrint 2025/1163) achieve arbitrary $T$ but require honest-majority and 37--136 rounds; Celi et al. (ePrint 2026/013) achieve dishonest-majority but are limited to $T \leq 6$. Our technique addresses the barrier that Lagrange coefficients grow as $Θ(q)$ for moderate $T$, making individual contributions too large for ML-DSA's rejection sampling. Unlike ECDSA threshold schemes where pairwise masks suffice for correctness, ML-DSA requires solving three additional challenges absent in prior work: (1) rejection sampling on $\|z\|_\infty$ must still pass after masking, (2) the $r_0$-check exposes $c s_2$ enabling key recovery if unprotected, and (3) the resulting Irwin-Hall nonce distribution must preserve EUF-CMA security. We solve all three. We instantiate this technique in three deployment profiles with full security proofs. Profile P1 (TEE-assisted) achieves 3-round signing with a trusted coordinator, with EUF-CMA security under Module-SIS. Profile P2 (fully distributed) eliminates hardware trust via MPC in 8 rounds, achieving UC security against malicious adversaries corrupting up to $n-1$ parties. Profile P3 (2PC-assisted) uses lightweight 2PC for the $r_0$-check in 3--5 rounds, achieving UC security under a 1-of-2 CP honest assumption with the best empirical performance (249ms). Our scheme requires $|S| \geq T+1$ signers and achieves success rates of 23--32\%, matching single-signer ML-DSA.

Related papers

• Trust Region Masking for Long-Horizon LLM Reinforcement Learning [20.589897184824878]
  Policy gradient methods for large language models optimize a surrogate objective computed from samples of a rollout policy.<n>When $_textroll ne _$, there is approximation error between the surrogate and the true objective.<n>We propose Trust Region Masking (TRM), which excludes entire sequences from gradient computation if any token violates the trust region.
  arXiv  Detail & Related papers  (2025-12-28T20:41:59Z)
• Spectral Sentinel: Scalable Byzantine-Robust Decentralized Federated Learning via Sketched Random Matrix Theory on Blockchain [0.0]
  Byzantine clients poison gradients under heterogeneous (Non-IID) data.<n>We propose Spectral Sentinel, a Byzantine detection and aggregation framework.<n>We implement the full system with blockchain integration on Polygon networks.
  arXiv  Detail & Related papers  (2025-12-14T09:43:03Z)
• Robust Conformal Prediction with a Single Binary Certificate [58.450154976190795]
  Conformal prediction (CP) converts any model's output to prediction sets with a guarantee to cover the true label with (adjustable) high probability.<n>We propose a robust conformal prediction that produces smaller sets even with significantly lower MC samples.
  arXiv  Detail & Related papers  (2025-03-07T08:41:53Z)
• Minimax-Optimal Multi-Agent Robust Reinforcement Learning [7.237817437521988]
  We extend the Q-FTRL algorithm citepli2022minimax to the RMGs in finite-horizon setting, assuming access to a generative model.<n>We prove that the proposed algorithm achieves an $varepsilon$-robust coarse correlated equilibrium (CCE) with a sample complexity (up to log factors) of $widetildeOleft(H3Ssum_i=1mA_iminleftH,1/Rright/varepsilon2right), where $S$ denotes the
  arXiv  Detail & Related papers  (2024-12-27T16:37:33Z)
• Masking Gaussian Elimination at Arbitrary Order, with Application to Multivariate- and Code-Based PQC [4.655421225385125]
  We provide a masking scheme for Gaussian Elimination (GE) with back substitution to defend against first- and higher-order attacks.<n>We propose a masked algorithm for transforming a system of linear equations into row-echelon form.<n>All novel gadgets are proven secure in the $t$-probing model.
  arXiv  Detail & Related papers  (2024-10-31T14:01:02Z)
• Projection by Convolution: Optimal Sample Complexity for Reinforcement Learning in Continuous-Space MDPs [56.237917407785545]
  We consider the problem of learning an $varepsilon$-optimal policy in a general class of continuous-space Markov decision processes (MDPs) having smooth Bellman operators. Key to our solution is a novel projection technique based on ideas from harmonic analysis. Our result bridges the gap between two popular but conflicting perspectives on continuous-space MDPs.
  arXiv  Detail & Related papers  (2024-05-10T09:58:47Z)
• Intention Analysis Makes LLMs A Good Jailbreak Defender [79.4014719271075]
  We present a simple yet highly effective defense strategy, i.e., Intention Analysis ($mathbbIA$)<n>$mathbbIA$ works by triggering LLMs' inherent self-correct and improve ability through a two-stage process.<n>Experiments on varying jailbreak benchmarks show that $mathbbIA$ could consistently and significantly reduce the harmfulness in responses.
  arXiv  Detail & Related papers  (2024-01-12T13:15:05Z)
• A Provably Secure Strong PUF based on LWE: Construction and Implementation [11.66624679713865]
  We construct a lattice PUF with provable security against ML attacks on classical and quantum computers. We prototype lattice PUF designs with $2136$ challenge-response pairs (CRPs) on a Spartan 6 FPGA. The resource-efficient design requires only $45$ slices for the PUF logic proper, and $351$ slices for a fuzzy extractor.
  arXiv  Detail & Related papers  (2023-03-05T23:41:00Z)
• Near Sample-Optimal Reduction-based Policy Learning for Average Reward MDP [58.13930707612128]
  This work considers the sample complexity of obtaining an $varepsilon$-optimal policy in an average reward Markov Decision Process (AMDP) We prove an upper bound of $widetilde O(H varepsilon-3 ln frac1delta)$ samples per state-action pair, where $H := sp(h*)$ is the span of bias of any optimal policy, $varepsilon$ is the accuracy and $delta$ is the failure probability.
  arXiv  Detail & Related papers  (2022-12-01T15:57:58Z)
• Reward-Mixing MDPs with a Few Latent Contexts are Learnable [75.17357040707347]
  We consider episodic reinforcement learning in reward-mixing Markov decision processes (RMMDPs) Our goal is to learn a near-optimal policy that nearly maximizes the $H$ time-step cumulative rewards in such a model.
  arXiv  Detail & Related papers  (2022-10-05T22:52:00Z)
• Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation [92.99933928528797]
  We study the model-based reward-free reinforcement learning with linear function approximation for episodic Markov decision processes (MDPs) In the planning phase, the agent is given a specific reward function and uses samples collected from the exploration phase to learn a good policy. We show that to obtain an $epsilon$-optimal policy for arbitrary reward function, UCRL-RFE needs to sample at most $tilde O(H4d(H + d)epsilon-2)$ episodes.
  arXiv  Detail & Related papers  (2021-10-12T23:03:58Z)
• Provably Efficient Safe Exploration via Primal-Dual Policy Optimization [105.7510838453122]
  We study the Safe Reinforcement Learning (SRL) problem using the Constrained Markov Decision Process (CMDP) formulation. We present an provably efficient online policy optimization algorithm for CMDP with safe exploration in the function approximation setting.
  arXiv  Detail & Related papers  (2020-03-01T17:47:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.