TY - JOUR
T1 - Machine Learning and Zero Knowledge Empowered Trustworthy Bitcoin Mixing for Next-G Consumer Electronics Payment
AU - Xu, Xijian
AU - Wu, Jun
AU - Bashir, Ali Kashif
AU - Omar, Marwan
N1 - Publisher Copyright:
© 1975-2011 IEEE.
PY - 2024/2/1
Y1 - 2024/2/1
N2 - In the next generation of consumer electronics, bitcoin mixing scheme is an essential part to realize decentralized anonymous payment. However, there are still some challenges in existing decentralized schemes. First, existing schemes necessitate broadcast of sensitive information for group construction and lack of machine learning models to make assisted decisions. Second, these schemes fall short in verification of consumers' integrity prior to their admission. Last but not least, the decentralized protocols tend to lack robust mechanisms for protecting the details of transactions during negotiations. To address the above challenges, this paper proposes a machine learning and zero knowledge empowered trustworthy bitcoin mixing for next-G consumer electronics payment to enhance consumer privacy and transaction details protection. Specifically, we design a mechanism based on zk-SNARKs for verifiable proofs to preserve privacy. Moreover, we construct a model based on machine learning to assist in decision making and verify the integrity by Pedersen commitments. Finally, proposed scheme refines an approach to guard transaction details during negotiations. The experiment demonstrates our approach offers enhanced efficiency and anonymity assurances without sacrificing performance.
AB - In the next generation of consumer electronics, bitcoin mixing scheme is an essential part to realize decentralized anonymous payment. However, there are still some challenges in existing decentralized schemes. First, existing schemes necessitate broadcast of sensitive information for group construction and lack of machine learning models to make assisted decisions. Second, these schemes fall short in verification of consumers' integrity prior to their admission. Last but not least, the decentralized protocols tend to lack robust mechanisms for protecting the details of transactions during negotiations. To address the above challenges, this paper proposes a machine learning and zero knowledge empowered trustworthy bitcoin mixing for next-G consumer electronics payment to enhance consumer privacy and transaction details protection. Specifically, we design a mechanism based on zk-SNARKs for verifiable proofs to preserve privacy. Moreover, we construct a model based on machine learning to assist in decision making and verify the integrity by Pedersen commitments. Finally, proposed scheme refines an approach to guard transaction details during negotiations. The experiment demonstrates our approach offers enhanced efficiency and anonymity assurances without sacrificing performance.
KW - Decentralized anonymous payment
KW - bitcoin
KW - consumer privacy
KW - mixing schemes
KW - zk-SNARKs
UR - http://www.scopus.com/inward/record.url?scp=85184335302&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85184335302&partnerID=8YFLogxK
U2 - 10.1109/TCE.2024.3361690
DO - 10.1109/TCE.2024.3361690
M3 - Article
AN - SCOPUS:85184335302
SN - 0098-3063
VL - 70
SP - 2210
EP - 2223
JO - IEEE Transactions on Consumer Electronics
JF - IEEE Transactions on Consumer Electronics
IS - 1
ER -