Abstract The majority of code-based cryptosystems are designed based on the syndrome decoding (SD) problems. The most effective attacks against SD problems employ information set decoding (ISD) algorithms. On EUROCRYPT 2023, Esser and Zweydinger presented a significant time-memory trade-off for the ISD algorithm. In this paper, we propose an improved ISD algorithm by combining Esser-Zweydinger algorithm and the nearest neighbor technique. Then, we apply the improved algorithm to reduce the memory of the depth-2 Both-May algorithm (2017 WCC) from $$O\big (2^{0.0282n}\big )$$ O ( 2 0.0282 n ) to $$O\big (2^{0.0249n}\big )$$ O ( 2 0.0249 n ) while keeping its time complexity constant at $$O\big (2^{0.0492n}\big )$$ O ( 2 0.0492 n ) , where n denotes the length of a binary linear code. Furthermore, we consider the quantum version of the improved algorithm and derive an asymptotic time complexity of $$O\big (2^{0.057866n}\big )$$ O ( 2 0.057866 n ) , which is lower than that of $$O\big (2^{0.058660n}\big )$$ O ( 2 0.058660 n ) from the quantum BJMM algorithm if memory consumption is not limited. In addition, we use the improved algorithm to reestimate the security level of HQC, BIKE, and Classic McEliece, respectively. In particular, for the parameters of Classic McEliece, the security levels estimated by our algorithm are 3 $$\sim$$ ∼ 6 bits lower than Esse-Zweydinger’s results.

Federated Learning (FL) is a distributed machine learning method that allows multiple devices to train models locally and aggregate updates at a central server to ensure data privacy. However, constructing a robust global model is challenging due to limited access to local data and heterogeneous client data. Furthermore, malicious clients and high communication and computation costs further complicate the design of FL algorithms. Existing methods for evaluating client contributions are computationally intensive, unsuitable for large-scale applications. To overcome these challenges, this paper proposes an adaptive weighting FL framework based on the Banzhaf index, providing a more efficient mechanism for evaluating and allocating client contributions. The Banzhaf index simplifies the computation process compared to traditional methods, allowing for better scalability. Additionally, we introduce a Neyman allocation stratified sampling strategy to optimize client selection, which effectively reduces variance and ensures balanced client representation, further enhancing both model performance and computational efficiency. Experimental results show that the proposed method significantly improves training efficiency, model accuracy, and robustness across various datasets, while reducing computation costs, making it well-suited for large-scale FL.

Personalized federated learning has gained significant attention due to its effectiveness in addressing data heterogeneity and achieving personalization. To train a model that performs well on local datasets, the key is to enable more effective knowledge sharing between clients and servers without compromising data privacy. Most current approaches aggregate partial model on central server for collaborative learning. However, the information conveyed through model parameters is limited. In this work, we propose a dual-information guided personalized federated learning approach called FedDCI. To obtain more collaborative information, we capture information from both model Parameters and data features. By implementing prototypes similarity-based aggregation, we generate client-specific global models and prototypes, effectively mitigating the challenges posed by data heterogeneity in Federated Learning. We conduct extensive experiments in both label skew and feature skew settings using four benchmark datasets. Experimental results demonstrate that FedDCI achieve accuracy improvements over existing methods in complex heterogeneous scenarios.

During the past few years, as Internet of Things develops rapidly, the applications of unmanned aerial vehicle (UAV) technology have been increasingly widely used. Meanwhile, the efficiency and security issues of UAV communication have brought about widespread attention of many researchers in different fields. In an open wireless network, the attacks on UAV communication mainly include eavesdropping, tampering, interrupting, forgery, etc. Signcryption is an appropriate method to address these security problems. However, the existing signcryption schemes are not suitable for UAVs with limited resources and ground station (GS) that needs to process a large number of messages from UAVs. Moreover, in a certain area, UAVs and a GS may belong to different cryptosystems. Therefore, in this article, we came up with a heterogeneous signcryption scheme for UAVs to communicate to a GS (HSC-U2G) without bilinear pairing operation, where UAVs belong to identity-based cryptography (IBC) while the GS belongs to public-key infrastructure (PKI). Moreover, our scheme provides identity privacy for UAVs and supports batch verification for the GS. Using the random oracle model, we demonstrate HSC-U2G can achieve insider security for confidentiality as well as unforgeability. After that, HSC-U2G is compared with several important related works and we provide all experimental results to show the efficiency.

Protein complexes are of great significance to provide valuable insights into the mechanisms of biological processes of proteins. A variety of computational algorithms have thus been proposed to identify protein complexes in a protein-protein interaction network. However, few of them can perform their tasks by taking into account both network topology and protein attribute information in a unified fuzzy-based clustering framework. Since proteins in the same complex are similar in terms of their attribute information and the consideration of fuzzy clustering can also make it possible for us to identify overlapping complexes, we target to propose such a novel fuzzy-based clustering framework, namely FCAN-PCI, for an improved identification accuracy. To do so, the semantic similarity between the attribute information of proteins is calculated and we then integrate it into a well-established fuzzy clustering model together with the network topology. After that, a momentum method is adopted to accelerate the clustering procedure. FCAN-PCI finally applies a heuristical search strategy to identify overlapping protein complexes. A series of extensive experiments have been conducted to evaluate the performance of FCAN-PCI by comparing it with state-of-the-art identification algorithms and the results demonstrate the promising performance of FCAN-PCI.

Complex networks have been widely adopted to represent a variety of complicated systems. Given a complex network, it is of great significance to perform accurate clustering for better understanding its intrinsic organization. To this end, a fuzzy-based clustering algorithm, i.e., FCAN, has been developed. Though effective, FCAN suffers from the disadvantage of slow convergence, which in return constrains its efficiency. To address this issue, this article proposes a fast fuzzy clustering algorithm, namely, F$^2$CAN, which incorporates a generalized momentum method into FCAN. Its fast convergence is rigorous justified in theory. Empirical studies on five datasets from real applications demonstrate that F$^2$CAN achieves a better performance when compared with FCAN and several state-of-the-art clustering algorithms in terms of convergence rate and clustering accuracy simultaneously. Hence, F$^2$CAN has potential for addressing the clustering analysis of large-scale complex networks emerging from industrial applications.

MOTIVATION: Clustering analysis in a biological network is to group biological entities into functional modules, thus providing valuable insight into the understanding of complex biological systems. Existing clustering techniques make use of lower-order connectivity patterns at the level of individual biological entities and their connections, but few of them can take into account of higher-order connectivity patterns at the level of small network motifs. RESULTS: Here, we present a novel clustering framework, namely HiSCF, to identify functional modules based on the higher-order structure information available in a biological network. Taking advantage of higher-order Markov stochastic process, HiSCF is able to perform the clustering analysis by exploiting a variety of network motifs. When compared with several state-of-the-art clustering models, HiSCF yields the best performance for two practical clustering applications, i.e. protein complex identification and gene co-expression module detection, in terms of accuracy. The promising performance of HiSCF demonstrates that the consideration of higher-order network motifs gains new insight into the analysis of biological networks, such as the identification of overlapping protein complexes and the inference of new signaling pathways, and also reveals the rich higher-order organizational structures presented in biological networks. AVAILABILITY AND IMPLEMENTATION: HiSCF is available at https://github.com/allenv5/HiSCF. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.