Digital signatures are fundamental to identity authentication and data integrity in cybersecurity, and the NIST-standardized Digital Signature Algorithm (DSA) frequently appears in the cryptography track of CTF competitions. However, DSA relies on number theory, modular arithmetic, and large-integer computation, making both the algorithm and its associated attacks difficult for beginners to follow. Conventional tools often expose only inputs and outputs, leaving the intermediate computations of signing, verification, and key-recovery attacks opaque. This paper presents a DSA signature analysis and visualisation platform tailored to CTF competitions. The platform provides three main capabilities: basic signature generation and verification, reproduction of common CTF attack methods, and dynamic visualisation of attack workflows. It covers three representative nonce vulnerabilities: nonce reuse, linear nonce leakage, and HNP-based lattice attacks. Stepwise displays and highlighted intermediate values make the underlying computations directly inspectable. Experiments show that the platform correctly reproduces the standard DSA workflow and all three attack scenarios.
Federated learning alleviates data silos through a “data-local, model-global” paradigm, but transmitting plaintext gradients exposes clients to reconstruction attacks from malicious servers. Existing secure aggregation methods face trade-offs among privacy, accuracy, and efficiency: homomorphic encryption incurs high overhead, differential privacy sacrifices accuracy, and lightweight secret-sharing schemes often lack weighted aggregation support and suffer accuracy degradation as client numbers grow. To address these limitations, we propose SecAGG, a lossless secure weighted aggregation scheme based on additive secret sharing. SecAGG adopts a three-tier architecture consisting of client clusters, cooperative servers, and a super server. Clients split weighted model parameters into random shares and distribute them to cooperative servers, which perform encrypted partial aggregation before the super server securely reconstructs the global model. Experimental results demonstrate that SecAGG achieves strict security against up to M-1 colluding servers under the semi-honest model while preserving FedAvg-equivalent accuracy with minimal computation and communication overhead, effectively balancing privacy, accuracy, and efficiency.
Xiaomei Tian· 2026 3rd World Conference on...· 0 citations
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