SearcharxivSearch

arXiv · 2510.22661

RejSCore: Rejection Sampling Core for Multivariate-based Public key Cryptography

Abstract

Post-quantum multivariate public key cryptography (MPKC) schemes resist quantum threats but require heavy operations, such as rejection sampling, which challenge resource-limited devices. Prior hardware designs have addressed various aspects of MPKC signature generation. However, rejection sampling remains largely unexplored in such contexts. This paper presents RejSCore, a lightweight hardware accelerator for rejection sampling in post-quantum cryptography. It specifically targets the QR-UOV scheme, which is a prominent candidate under the second-round of the National Institute of Standards and Technology (NIST) additional digital signature standardization process. The architecture includes an AES-CTR-128-based pseudorandom number generator. Moreover, a lightweight iterative method is employed in rejection sampling, offering reduced resource consumption and area overhead while slightly increasing latency. The performance of RejSCore is comprehensively evaluated on Artix-7 FPGAs and 65 nm CMOS technology using the Area-Delay Product (ADP) and Power-Delay Product (PDP). On Artix-7 and 65 nm CMOS, RejSCore achieves an area of 2042 slices and 464,866~$\mu m^2$, with operating frequencies of 222 MHz and 565 MHz, respectively. Using the QR-UOV parameters for security level I ($q = 127$, $v = 156$, $m = 54$, $l = 3$), the core completes its operation in 8525 clock cycles. The ADP and PDP evaluations confirm RejSCore's suitability for deployment in resource-constrained and security-critical environments.

Explore related subjects

Keep this discovery

BibTeXRIS

Malik Imran, Safiullah Khan, Zain Ul Abideen, Ciara Rafferty, Ayesha Khalid, Muhammad Rashid, Maire O'Neill. 2025-10-26. RejSCore: Rejection Sampling Core for Multivariate-based Public key Cryptography. https://arxiv.org/abs/2510.22661

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Empirical Evaluation of Membership Inference Attacks on NLP Text Classifiers: A Baseline Study on SST-2

Membership inference attacks (MIAs) try to determine whether a specific record was used to train a model, a privacy risk that matters in natural language processing (NLP), where training data can contain sensitive user text. This paper presents a controlled benchmark of membership inference vulnerability for text classification on the GLUE SST-2 sentiment dataset. A TF-IDF + Logistic Regression pipeline and a fine-tuned DistilBERT classifier are compared under a loss-threshold MIA, with utility measured by development accuracy and macro F1. DistilBERT reached 0.9466 accuracy and 0.9460 macro F1 against 0.8756 and 0.8727 for Logistic Regression, yet both models leaked membership signal (Attack AUC 0.5615 and 0.5800, respectively). Two mitigations were tested. Stronger regularization reduced leakage for Logistic Regression at a visible utility cost, whereas fine-tuning DistilBERT for 2 epochs instead of 3 reduced leakage with negligible accuracy loss. Lightweight training adjustments can improve the privacy-utility trade-off without complex defenses.

cs.CR

Empirical Evaluation of Data Poisoning Attacks in Supervised Learning

Data poisoning corrupts training data to degrade a model or to plant attacker-controlled behavior. This study evaluates two representative training-time attacks, label flipping and backdoor poisoning, on MNIST and Fashion-MNIST with three baseline classifiers: Logistic Regression, Linear SVM, and Random Forest. Clean training is compared with poisoning rates of 5%, 10%, and 20% using clean-test accuracy, macro-precision, macro-recall, macro-F1, and, for backdoors, attack success rate. Label flipping caused clear degradation, largest for Logistic Regression and Linear SVM, while Random Forest stayed comparatively stable. Backdoor poisoning reached attack success rates from 0.9667 to 1.0000 on both datasets and all three models while often keeping clean-test performance near baseline. The results separate indiscriminate poisoning, which shows up in standard metrics, from targeted backdoor poisoning, which stays comparatively stealthy while embedding highly effective malicious behavior, and they support security-oriented evaluation beyond conventional clean-test metrics.

cs.CR

DeFiFusion: Combining Transaction Events with Smart Contracts to Detect Price Manipulation Attacks

Decentralized Finance (DeFi) has emerged as a rapidly growing blockchain-based financial service, where market transaction dynamics and underlying smart contract logic are intricately intertwined. This autonomous interplay, while eliminating centralized intermediaries, significantly expands the vulnerability surface of DeFi protocols to Price Manipulation Attacks (PMAs), which have already inflicted catastrophic financial losses. Despite their gravity, existing detection paradigms suffer from fundamental limitations. Transaction-centric methods lack awareness of contract execution semantics, making them prone to false positives under legitimate market volatility, while static contract analyses ignore real transaction behaviors and frequently report vulnerabilities that are infeasible to exploit in practice. We present DeFiFusion, a dual-modal PMA detection framework that closes this gap by jointly modeling transaction events and smart contract semantics within a unified pipeline. Our core insight is that PMA maliciousness emerges only from the interaction between transaction behaviors and the contract logic they exploit; neither signal suffices in isolation. Accordingly, we derive price-manipulation-aware event encoding for extracting fine-grained temporal and economic features tailored to manipulation patterns. We further introduce LLM-based contract semantic extraction to supply the execution-logic context that prior behavioral methods lack. To fuse these modalities, we propose a Dual-Modal Projection-Fusion Transformer with T5-style relative positional encoding, capturing the cyclic multi-stage execution structures that distinguish PMAs from benign market activity. Extensive experiments demonstrate that DeFiFusion consistently achieves state-of-the-art detection performance, effectively recalling 222 of the 225 PMA cases while maintaining a precision of 96.10%.

cs.CR