Machine Learning and Zero Knowledge Empowered Trustworthy Bitcoin Mixing for Next-G Consumer Electronics Payment-Reference-Cited by-同舟云学术

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Machine Learning and Zero Knowledge Empowered Trustworthy Bitcoin Mixing for Next-G Consumer Electronics Payment

Published:2024-02 Issue:1 Volume:70 Page:2210-2223
ISSN:0098-3063
Container-title:IEEE Transactions on Consumer Electronics
language:
Short-container-title:IEEE Trans. Consumer Electron.

Author:

Xu Xijian¹,Wu Jun¹^ORCID,Bashir Ali Kashif²^ORCID,Omar Marwan³^ORCID

Affiliation:

1. Graduate School of Information, Production and System, Waseda University, Fukuoka, Japan

2. Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, U.K.

3. Information Technology and Management, Illinois Institute of Technology, Warrenville, IL, USA

Funder

Japan Society for the Promotion of Science (JSPS) KAKENHI

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/30/10510045/10419042.pdf?arnumber=10419042

Reference37 articles.

1. JPEG Decoding With Nonlinear Inverse Transform Network and Progressive Recurrent Residual Network

2. State-of-the-Art and Research Opportunities for Next-Generation Consumer Electronics

3. An Explainable and Resilient Intrusion Detection System for Industry 5.0

4. Zerocash: Decentralized Anonymous Payments from Bitcoin

5. Bitcoin: A peer-to-peer electronic cash system;Nakamoto

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