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Titlebook: Blockchain and Trustworthy Systems; 5th International Co Jiachi Chen,Bin Wen,Ting Chen Conference proceedings 2024 The Editor(s) (if applic

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樓主: damped
41#
發(fā)表于 2025-3-28 15:00:29 | 只看該作者
42#
發(fā)表于 2025-3-28 19:02:32 | 只看該作者
https://doi.org/10.1007/978-3-540-79862-0sers’ search intentions, behavior, and interests from access patterns, which can be used for targeted attacks, phishing, or identity theft. Access pattern disclosure may also jeopardize the confidentiality of the searched data, as it may reveal sensitive information related to the queried data..Trad
43#
發(fā)表于 2025-3-28 23:56:31 | 只看該作者
Willy Schneider,Alexander Hennigfor consortium blockchain faces serious challenges: Firstly, due to the sealied feature of consortium blockchain, it should build up the effective and robust computation mechanism for distributed supervision between various platforms; Secondly, due to the sensitivity of supervision data, data transm
44#
發(fā)表于 2025-3-29 06:35:35 | 只看該作者
Willy Schneider,Alexander Hennign security management. Because of this, the application of a blockchain-based collaborative healthcare system has been born, and blockchain has become the main driving force for the development of collaborative healthcare. However, the existing blockchain HIS (Hospital Management Information System)
45#
發(fā)表于 2025-3-29 09:47:56 | 只看該作者
A General Smart Contract Vulnerability Detection Framework with?Self-attention Graph Poolingous common vulnerabilities via a uniform framework. We leveraged the Abstract Syntax Trees (AST) and self-attention-based graph pooling models to generate topological graphs from smart contract code analysis. We adopted Graph Neural Networks for vulnerability detection. Experimental results demonstr
46#
發(fā)表于 2025-3-29 14:03:00 | 只看該作者
47#
發(fā)表于 2025-3-29 15:51:47 | 只看該作者
MF-Net: Encrypted Malicious Traffic Detection Based on?Multi-flow Temporal Features, we use a public dataset provided by Qi An Xin for experimental evaluation. Experimental results show that MF-Net outperforms Graph Neural Network based multi-flow method. MF-Net can achieve 98.13% accuracy and 98.10% F1 score using 5 flows, which enables effective encrypted malicious traffic detec
48#
發(fā)表于 2025-3-29 21:40:24 | 只看該作者
ePoW Energy-Efficient Blockchain Consensus Algorithm for?Decentralize Federated Learning System in?Rment metrics to facilitate the selection of participating nodes in the ePoW competition. Following a successful BFL global generation event, the dynamic difficulty adjustment mechanism collaborates with the engagement metrics to identify the most reliable node and mitigate resource consumption durin
49#
發(fā)表于 2025-3-30 01:36:04 | 只看該作者
50#
發(fā)表于 2025-3-30 04:56:43 | 只看該作者
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