Multi-designated verifier signatures (MDVS) are a signer privacy-preserving signature scheme where non-designated parties cannot tell the difference between signatures produced by the true signer and the designated verifiers. However, such privacy-preservation does not allow even the signer to disclose that her signature has been actually created by herself. Thus, it is worth giving to a signer of MDVS claimability, the ability to later make a claim that convinces anyone including non-designated third parties that a signature has been created by the actual signer. In this paper, we propose such a scheme, claimable multi-designated verifier signatures (CMDVS). We define a syntax and security requirements for CMDVS and propose the generic constructions of two types of CMDVS. The first one consists of standard signatures, ring signatures, commitment schemes, and pseudorandom functions (PRF). The second one is the transformation from the first one, using one-time signatures, public key encryption schemes, PRF and non-interactive zero-knowledge proofs.
With the rapid proliferation of wireless devices, ensuring secure communication over open wireless channels has become increasingly critical. Physical layer key generation has emerged as a lightweight and information-theoretically secure cryptographic mechanism, offering a promising complement to traditional cryptographic approaches. However, in Internet of Things (IoT) scenarios characterized by heterogeneous devices and highly dynamic environments, conventional physical-layer key generation methods suffer from low key generation rates, poor stability, and limited adaptability. To address these challenges, this paper proposes a novel AI-based physical layer key generation framework that integrates multi-source signal fusion and intelligent feature selection. Specifically, deep learning models are employed to fuse features from multiple heterogeneous wireless signal sources, enabling the extraction of high-quality randomness and enhancing key entropy and security. In parallel, an attention mechanism is employed to dynamically select the most suitable physical layer features based on real-time environmental conditions, thereby enhancing the system’s adaptability and robustness in complex IoT settings. Finally, we outline potential future research directions and discuss the feasibility of implementing the proposed framework in real-world deployments.
The advent of quantum computing has intensified concerns about the security of widely deployed cryptographic primitives, including RSA and elliptic curve cryptography. In turn, this has heightened demand for next‑generation cryptographic algorithms capable of withstanding quantum attacks, with lattice‑based cryptography emerging as a leading candidate for both encryption and signature schemes. The security of lattice‑based cryptography is grounded in the Shortest Vector Problem (SVP), whose hardness remains unresolved despite numerous proposed algorithms aimed at assessing or attacking it. Among algorithms for tackling SVP, the Sieve family is characterized by a trade‑off between running time and space complexity. In this paper, we introduce the Ideal Triple Sieve, which combines the space efficiency of the Tuple Minkowski Sieve with the rotational advantages of the Ideal Gauss Sieve. The algorithm is designed for broad applicability and, in particular, achieves superior performance on prime cyclotomic ideal lattices, where it outperforms the Ideal Gauss Sieve. Consequently, it yields Minkowski‑reduced lattice vectors whose quality is substantially higher than that of Gauss‑reduced vectors. At the same time, the Ideal Triple Sieve retains the same asymptotic space complexity as the Ideal Gauss Sieve, O(1/n2^0.1887n+o(n)) , and its running time is O(n^22^0.4812n+o(n)) . In addition, we develop a set of techniques that exploit structural properties of ideal lattices to further improve the efficiency of the Ideal Triple Sieve. These techniques enable faster evaluation of norms and inner products and eliminate unnecessary reduction steps, thereby markedly reducing execution time. Finally, we present experimental results for our proposal, demonstrating efficiency compared with existing methods.
Botnet intrusion detection faces several critical challenges, including data silos, significant class imbalance, privacy concerns, and non-independent and identically distributed (Non-IID) client data. To address these challenges, we propose a novel intrusion detection framework based on federated learning. Our framework allows isolated data holders to collaboratively train a shared model while preserving data privacy. To mitigate aggregation bias caused by statistical heterogeneity in the federated setting, we introduce a Jensen-Shannon (JS) divergence-based client similarity-aware aggregation strategy, which dynamically adjusts each client's contribution during global model updates. Furthermore, we integrate the Synthetic Minority Over-sampling Technique (SMOTE) and Focal Loss into the local training process to enhance the model's capability in detecting minority-class attack traffic. Extensive experiments conducted on the publicly available CTU-13 botnet dataset demonstrate that our approach significantly outperforms both conventional centralized models and standard federated learning baselines in terms of accuracy, F1-score, and other key metrics. The results highlight the framework's effectiveness in privacy-preserving modeling of imbalanced and Non-IID intrusion detection data.
Data protection and verification are constantly at risk, and blockchain technology is trying to address this problem effectively. This technology fundamentally changed how we perceive our online safety. Two widely recognized blockchain technology variations namely ethereum and lightweight will be examined in the following paper. The two technologies will be compared with respect to execution time, gas prices, storage efficiency, and scalability. Our research will validate digital data using small to large datasets and propose the best framework for large scale applications. According to our findings, the lightweight blockchain increases the efficiency of gas consumption by 40
