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Titlebook: Blockchain-Based Data Security in Heterogeneous Communications Networks; Dongxiao Liu,Xuemin (Sherman) Shen Book 2024 The Editor(s) (if ap

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發(fā)表于 2025-3-23 13:39:34 | 只看該作者
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發(fā)表于 2025-3-23 16:25:02 | 只看該作者
Reliable Data Provenance in HCN,analysis of network errors. As the future networks are embracing a distributed and heterogeneous architecture, reliable data provenance across network trust domains become a challenging issue. In this chapter, we investigate the blockchain-based data provenance approach in HCN. First, we review the
13#
發(fā)表于 2025-3-23 18:31:44 | 只看該作者
Transparent Data Query in HCN,lays a vital role in supporting many data-intensive applications in future networks. As data are generated and distributed at heterogeneous network entities, data query is often conducted by a third party that is out of the trust domain of the query user. In this chapter, we investigate transparent
14#
發(fā)表于 2025-3-24 01:45:15 | 只看該作者
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發(fā)表于 2025-3-24 03:59:19 | 只看該作者
Conclusion and Future Works,ecurity approaches: Reliable data provenance, transparent data query, and fair data marketing are discussed, which not only realize a decentralized solution but address the efficiency, privacy, and fairness challenges with a blockchain architecture. Then, we investigate potential research directions
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發(fā)表于 2025-3-24 06:50:21 | 只看該作者
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發(fā)表于 2025-3-24 11:56:46 | 只看該作者
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發(fā)表于 2025-3-24 18:25:35 | 只看該作者
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發(fā)表于 2025-3-24 19:36:57 | 只看該作者
Liberalism and Suffrage, 1866–85lution but address the efficiency, privacy, and fairness challenges with a blockchain architecture. Then, we investigate potential research directions, including on/off-chain computation models with modular designs, and multi-party fair AI model sharing with efficient verifications.
20#
發(fā)表于 2025-3-25 01:18:42 | 只看該作者
Conclusion and Future Works,lution but address the efficiency, privacy, and fairness challenges with a blockchain architecture. Then, we investigate potential research directions, including on/off-chain computation models with modular designs, and multi-party fair AI model sharing with efficient verifications.
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