Recent years have witnessed the surge of academic researches and industrial implementations of succinct non-interactive arguments of knowledge (SNARKs). However, proving time remains a bottleneck for applying SNARKs to large-scale circuits. To accelerate the proof generation process, a promising way is to distribute the workload to several machines running in parallel, the SNARKs with which feature are called distributed SNARKs. Nevertheless, most existing works either require a trusted setup, or rely on quantum-insecure assumptions, or suffer from linear communication costs.

MOTIVATION: Large-scale prospective cohort studies collect longitudinal biospecimens alongside time-to-event outcomes to investigate biomarker dynamics in relation to disease risk. The nested case-control (NCC) design provides a cost-effective alternative to full cohort biomarker studies while preserving statistical efficiency. Despite advances in joint modeling for longitudinal and time-to-event outcomes, few approaches address the unique challenges posed by NCC sampling, non-normally distributed biomarkers, and competing survival outcomes. RESULTS: Motivated by the TEDDY study, we propose "JM-NCC", a joint modeling framework designed for NCC studies with competing events. It integrates a generalized linear mixed-effects model for potentially non-normally distributed biomarkers with a cause-specific hazard model for competing risks. Two estimation methods are developed. fJM-NCC leverages NCC sub-cohort longitudinal biomarker data and full cohort survival and clinical metadata, while wJM-NCC uses only NCC sub-cohort data. Both simulation studies and an application to TEDDY microbiome dataset demonstrate the robustness and efficiency of the proposed methods. AVAILABILITY AND IMPLEMENTATION: Software is available at https://github.com/Zhaoyn-oss/JMNCC and archived on Zenodo at https://zenodo.org/records/18199759 (DOI: 10.5281/zenodo.18199759).

Threshold ECDSA schemes distribute the capability of issuing signatures to multiple parties. They have been used in practical MPC wallets holding cryptocurrencies. However, most prior protocols are not robust, wherein even one misbehaving or non-responsive party would mandate an abort. Robust schemes have been proposed (Wong et al., NDSS ’23, ’24), but they do not match state-of-the-art number of rounds which is only three (Doerner et al., S&P ’24). In this work, we propose robust threshold ECDSA schemes RompSig-Q and RompSig-L that each take three rounds (where the first two are broadcasts, whereas the non-robust scheme of Doerner et al. uses no broadcasts). Building on the works of Wong et al. and further optimized towards saving bandwidth, they respectively take each signer (1.0t+ 1.6) KiB and 3.0 KiB outbound broadcast communication, and thus exhibit bandwidth efficiency that is competitive in practical scenarios where broadcasts are natively handled. RompSig-Q preprocesses multiplications and features fast online signing; RompSig-L leverages threshold CL encryption for scalability and dynamic participation.

Recent advances in Vector Oblivious Linear Evaluation (VOLE) protocols have enabled constant-round, fast, and scalable (designated-verifier) zero-knowledge proofs, significantly reducing prover computational cost. Existing protocols, such as QuickSilver [CCS'21] and LPZKv2 [CCS'22], achieve efficiency with prover costs of 4 multiplications in the extension field per AND gate for Boolean circuits, with one multiplication requiring a O (k log k) -bit operation where k== 128 is the security parameter, and 3–4 field multiplications per multiplication gate for arithmetic circuits over a large field. We introduce JesseQ, a suite of two VOLE-based protocols: JQv1 and JQv2, which advance state of the art. JQv1 requires only 2 scalar multiplications in an extension field per AND gate for Boolean circuits, with one scalar needing a$O(\kappa)$bit operation, and 2 field multiplications per multiplication gate for arithmetic circuits over a large field. In terms of communication costs, JQv1 needs just 1 field element per gate. JQv2 further reduces communication costs by half at the cost of doubling the prover's computation. Experiments show that, compared to the current state of the art, both JQv1 and JQv2 achieve at least 3.9× improvement in the online phase for Boolean circuits. For large field circuits, JQv1 has a similar performance, while JQv2 offers a 1.3× improvement. Additionally, both JQv1 and JQv2 maintain the same communication cost as the current state of the art. No-tably, on the cheapest AWS instances, JQv1 can prove 9.2 tril-lion AND gates (or 5.8 trillion multiplication gates over a 61-bit field) for just one US dollar. JesseQ excels in applications like inner products, matrix multiplication, and lattice problems, delivering 40% – 200% performance improvements compared to QuickSilver. Additionally, JesseQ integrates seamlessly with the sublinear Batchman framework [CCS'23], enabling further efficiency gains for batched disjunctive statements.

The Paillier cryptosystem is renowned for its applications in electronic voting, threshold ECDSA, multi-party computation, and more, largely due to its additive homomorphism. In these applications, range proofs for the Paillier cryptosystem are crucial for maintaining security, because of the mismatch between the message space in the Paillier system and the operation space in application scenarios.

Threshold ECDSA receives interest lately due to its widespread adoption in blockchain applications. A common building block of all leading constructions involves a secure conversion of multiplicative shares into additive ones, which is called the multiplicative-to-additive (MtA) function. MtA dominates the overall complexity of all existing threshold ECDSA constructions. Specifically, O(n2) invocations of MtA are required in the case of n active signers. Hence, improvement of MtA leads directly to significant improvements for all state-of-the-art threshold ECDSA schemes.

Verifiably encrypted signature (VES) allows a signer to encrypt a signature under the public key of a trusted third party (aka adjudicator) in a verifiable manner. Recently, Yanget al.proposed a practical verifiably encrypted signature scheme for ECDSA and initiated the study of escrow protocol for Bitcoin via VES. This paper generalizes and improves the VES scheme of Yanget al., such that it covers a family of signatures with similar structures, including ECDSA, Schnorr and their variants. Our construction is very efficient: comparing with Yanget al.’s construction, the size of the resulting VES (for ECDSA) is reduced by more than 25 times. The only caveat is that the adjudicator is required to store a look-up table of size around 270MB. Our scheme naturally gives rise to escrow protocols for mainstream cryptocurrencies that employ ECDSA-like signatures to authorise transaction, including Bitcoin, Ethereum, Cardano, Chainlink, etc.

Biomarkers predict World Trade Center-Lung Injury (WTC-LI); however, there remains unaddressed multicollinearity in our serum cytokines, chemokines, and high-throughput platform datasets used to phenotype WTC-disease. To address this concern, we used automated, machine-learning, high-dimensional data pruning, and validated identified biomarkers. The parent cohort consisted of male, never-smoking firefighters with WTC-LI (FEV1, %Pred< lower limit of normal (LLN); n = 100) and controls (n = 127) and had their biomarkers assessed. Cases and controls (n = 15/group) underwent untargeted metabolomics, then feature selection performed on metabolites, cytokines, chemokines, and clinical data. Cytokines, chemokines, and clinical biomarkers were validated in the non-overlapping parent-cohort via binary logistic regression with 5-fold cross validation. Random forests of metabolites (n = 580), clinical biomarkers (n = 5), and previously assayed cytokines, chemokines (n = 106) identified that the top 5% of biomarkers important to class separation included pigment epithelium-derived factor (PEDF), macrophage derived chemokine (MDC), systolic blood pressure, macrophage inflammatory protein-4 (MIP-4), growth-regulated oncogene protein (GRO), monocyte chemoattractant protein-1 (MCP-1), apolipoprotein-AII (Apo-AII), cell membrane metabolites (sphingolipids, phospholipids), and branched-chain amino acids. Validated models via confounder-adjusted (age on 9/11, BMI, exposure, and pre-9/11 FEV1, %Pred) binary logistic regression had AUCROC [0.90(0.84-0.96)]. Decreased PEDF and MIP-4, and increased Apo-AII were associated with increased odds of WTC-LI. Increased GRO, MCP-1, and simultaneously decreased MDC were associated with decreased odds of WTC-LI. In conclusion, automated data pruning identified novel WTC-LI biomarkers; performance was validated in an independent cohort. One biomarker-PEDF, an antiangiogenic agent-is a novel, predictive biomarker of particulate-matter-related lung disease. Other biomarkers-GRO, MCP-1, MDC, MIP-4-reveal immune cell involvement in WTC-LI pathogenesis. Findings of our automated biomarker identification warrant further investigation into these potential pharmacotherapy targets.

SAR image is hard to be interpreted by users compared with optical remote sensing image. And current automatic SAR image interpretation cannot meet the requirement of practical application. In order to overcome this problem to some extent, this article presents a new SAR image representation method called "SAR image re-imaging". This second imaging SAR image is composed from the underlying information extracted or learned from the original SAR image and is displayed in the YCbCr color space. Experimental results demonstrate that our re-imaging SAR images are more readable and more intelligible.