Abstract The RSA (Rivest–Shamir–Adleman) cryptosystem is a widely used public-key cryptographic algorithm in information systems and computer applications. With the advancement of lattice theory, a technique known as the lattice-based method has emerged as a significant threat to RSA and its variants. This paper aims to conduct a bibliometric analysis of publications in the field of lattice-based attacks on RSA-type cryptosystems. The analysis is based on a dataset of relevant publications retrieved from Scopus and utilizes visualization tools such as CiteSpace and VOSviewer for a thorough overview. In order to understand the research developmental trajectory, we analyze the publication trends over the years, explore cooperation networks at various levels, including country/region, institution, and author, and assess the extent of collaboration, contribution, and productivity within the field. Additionally, author co-citation network and reference co-citation clustering are utilized to enable the identification of significant research achievements, cutting-edge developments, and structural framework. Furthermore, we conduct detailed analysis from a keyword perspective to identify research hotspots and emerging trends. The findings contribute to the existing body of knowledge on lattice-based cryptanalysis of RSA-type cryptosystems. Moreover, this bibliometric analysis serves as a valuable resource for identifying fruitful areas for further exploration and guides future research efforts.

This paper focuses on the RSA-polynomial problem, a cryptographic hard problem that has been recently proposed and studied in, along with its various applications. We revisit this problem and conduct a refined analysis to address an ambiguous condition that was previously introduced in the context of RSA-polynomial based semiprime factorization. By deriving an accurate attack condition, we are able to identify weak cases of the RSA-polynomial problem and expand the vulnerable bound. To facilitate this, we propose two optimized factoring attacks that leverage improved lattice-based theorems for solving bivariate integer polynomials of a specific form. The validity and effectiveness of our proposed factoring attacks are verified through both theoretical analysis and experimental results. Additionally, we examine the RSA-polynomial based commitment scheme and identify deficiencies that compromise its reliability. To address the limitations, we propose enhancements to the commitment phase of the scheme.

Persistent Fault Attack (PFA) is a recently proposed Fault Attack (FA) method in CHES 2018. It is able to recover full AES secret key in the Single-Byte-Fault scenario. It is demonstrated that classical FA countermeasures, such as Dual Modular Redundancy (DMR) and mask protection, are unable to thwart PFA. In this paper, we propose a fast-detection and fault-correction algorithm to prevent PFA. We construct a fixed input and output pair to detect faults rapidly. Then we build two extra redundant tables to store the relationship between the adjacent elements in the S-box, by which the algorithm can correct the faulty elements in the S-box. Our experimental results show that our algorithm can effectively prevent PFA in both Single-Byte-Fault and Multiple-Bytes-Faults scenarios. Compared with the classical FA countermeasures, our algorithm has a much better effect against PFA. Further, the time cost of our algorithm is 40% lower than the classical FA countermeasures.

In recent years, various deep learning techniques have been exploited in side channel attacks, with the anticipation of obtaining more satisfactory attack results. Most of them con-centrate on improving network architectures or putting forward novel metrics, assuming that there are adequate profiling traces available to train an appropriate neural network. However, in practical scenarios, profiling traces are probably insufficient, which makes the network learn deficiently and compromises attack performance. In this paper, we investigate a kind of data augmentation technique, called mixup, and first propose to exploit it in deep-learning based side channel attacks, for the purpose of expanding the profiling set and facilitating the chances of mounting a successful attack. We utilize mixup to generate new traces and perform Correlation Power Analysis for generated traces and original traces. The analysis reveals that the leakage location and leakage intensity between them are consistent. In view of this observation, we consider it feasible to add generated traces to the original profiling set. Our verifying experiments show that mixup is truly capable of enhancing attack performance especially for insufficient profiling traces. Specifically, when the size of the training set is decreased to 30% of the whole set, mixup can almost reduce required attacking traces to half. We test three mixup parameter values and conclude that generally all of them can bring about improvements. Besides, we compare three leakage models and surprisingly discover that least significant bit model, which is less frequently used in previous works, actually surpasses prevalent identity model and hamming weight model in terms of attack results.