Research on Blockchain Transaction Privacy Protection Methods Based on Deep Learning

Jun Li (), Chenyang Zhang, Jianyi Zhang and Yanhua Shao
Additional contact information
Jun Li: School of Information Management, Beijing Information Science and Technology University, Beijing 100192, China
Chenyang Zhang: School of Information Management, Beijing Information Science and Technology University, Beijing 100192, China
Jianyi Zhang: School of Computing and Informatics, The University of Louisiana at Lafayette, Lafayette, LA 70504, USA
Yanhua Shao: National Computer System Engineering Research Institute of China, Beijing 100083, China

Future Internet, 2024, vol. 16, issue 4, 1-20

Abstract: To address the challenge of balancing privacy protection with regulatory oversight in blockchain transactions, we propose a regulatable privacy protection scheme for blockchain transactions. Our scheme utilizes probabilistic public-key encryption to obscure the true identities of blockchain transaction participants. By integrating commitment schemes and zero-knowledge proof techniques with deep learning graph neural network technology, it provides privacy protection and regulatory analysis of blockchain transaction data. This approach not only prevents the leakage of sensitive transaction information, but also achieves regulatory capabilities at both macro and micro levels, ensuring the verification of the legality of transactions. By adopting an identity-based encryption system, regulatory bodies can conduct personalized supervision of blockchain transactions without storing users’ actual identities and key data, significantly reducing storage computation and key management burdens. Our scheme is independent of any particular consensus mechanism and can be applied to current blockchain technologies. Simulation experiments and complexity analysis demonstrate the practicality of the scheme.

Keywords: deep learning; blockchain; privacy protection; regulatory functionality; cryptography (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/16/4/113/pdf (application/pdf)
https://www.mdpi.com/1999-5903/16/4/113/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:16:y:2024:i:4:p:113-:d:1365621

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().