Title: Constructive system for double-spend data detection and prevention in inter and intra-block of blockchain
Authors: J. Vijayalakshmi; A. Murugan
Addresses: Department of Computer Science, Dr. Ambedkar Government Arts College (Autonomous), India; Affiliated to: University of Madras, India ' Department of Computer Science, Dr. Ambedkar Government Arts College (Autonomous), India; Affiliated to: University of Madras, India
Abstract: Currently, our global financial market faces lots of trouble due to migration from fiat currency to cryptocurrency and its underlying blockchain technology. Blockchain provides trust in a decentralised way for storing, managing, and retrieving the transactions. The double-spending issue arises due to the erroneous transaction verification mechanism in the blockchain. Research has shown that transaction malleability like double-spending creates millions of bitcoins losses to the owners as well as few bitcoin exchanges. This research aims to detect and prevent the double-spending of bitcoins in single and multiple blocks. In this context, double-spend data in a single block is identified using the DPL2A method. Further, the original transaction from the double-spend transaction list is identified using the ACRT method which acts as a prevention of double-spend in a forthcoming occurrence. Similarly, double-spend data in multiple blocks are identified using MBDTD along with the Cognizant Merkle tree. Finally, a system named F2DP is constructed to detect and prevent the double-spend data in inter and intra blocks of the blockchain. The result indicates these methods will act best for double-spend detection and prevention with a limited set of transaction records. Further research is needed to increase the scalability of transaction records.
Keywords: cryptocurrency; bitcoin; double-spending; UTXO; Merkle.
DOI: 10.1504/IJCSE.2021.119967
International Journal of Computational Science and Engineering, 2021 Vol.24 No.6, pp.551 - 562
Received: 25 Mar 2020
Accepted: 09 Jan 2021
Published online: 04 Jan 2022 *