夸张 发表于 2025-3-23 13:39:34
http://reply.papertrans.cn/19/1893/189268/189268_11.pngbarium-study 发表于 2025-3-23 16:25:02
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 theCLAIM 发表于 2025-3-23 18:31:44
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怎样才咆哮 发表于 2025-3-24 01:45:15
http://reply.papertrans.cn/19/1893/189268/189268_14.pngacquisition 发表于 2025-3-24 03:59:19
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 directionsValves 发表于 2025-3-24 06:50:21
http://reply.papertrans.cn/19/1893/189268/189268_16.pngVertebra 发表于 2025-3-24 11:56:46
http://reply.papertrans.cn/19/1893/189268/189268_17.png荨麻 发表于 2025-3-24 18:25:35
http://reply.papertrans.cn/19/1893/189268/189268_18.png灿烂 发表于 2025-3-24 19:36:57
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.慢慢冲刷 发表于 2025-3-25 01:18:42
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.