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发表于 2025-3-25 13:40:12
Meng Shen,Xiangyun Tang,Liehuang ZhuDelivers a comprehensive introduction of security and privacy in Web 3.0, along with practical defense frameworks.Explores privacy computing, data asset protection, and behavior analysis within the ar
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发表于 2025-3-25 17:29:47
Digital Privacy and Securityhttp://image.papertrans.cn/s/image/863529.jpg
Repatriate
发表于 2025-3-25 22:04:06
https://doi.org/10.1007/978-981-97-5752-7Web 3; 0;Blockchain; Privacy Protection; Privacy Computing; Federated Learning, ; Device Authentication;
方舟
发表于 2025-3-26 00:07:53
978-981-97-5754-1The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
Ingenuity
发表于 2025-3-26 07:13:46
Security and Privacy in Web 3.0978-981-97-5752-7Series ISSN 2731-992X Series E-ISSN 2731-9938
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Basilar-Artery
发表于 2025-3-26 15:59:44
Security and Privacy Defense Framework for Web 3.0,.0, while offering numerous advantages, presents unique challenges such as ensuring privacy protection, authentication and behavior identification, and the auditing of data asset transactions. To navigate these complexities, we propose a comprehensive security and privacy defense framework for Web 3
索赔
发表于 2025-3-26 17:31:49
Verifiable Privacy-Preserving Federated Learning in Web 3.0,plete sovereignty and ownership over their data in Web 3.0. However, the high degree of personal control over data limits the mobility and interoperability of data, forming data silos that restrict the development of Web 3.0. As an advanced paradigm that breaks down data silos, federated learning ca