Trusted Player Transfer Evaluation for Sport Markets Based on Blockchain and Locality-Sensitive Hashing

• Published in: IEEE access Vol. 9; pp. 87332 - 87339
• Main Authors: Liu, Chao, Li, Zengxi, Liu, Shunshun, Xie, Jushi, Yan, Chao, Huang, Wanli
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Blockchain; Business; Business competition; Collaboration; Cryptography; Fans; Indexes; locality-sensitive hashing; Market value; Performance evaluation; Player transfer evaluation; Privacy; privacy-preservation; Spectators; sport market; Sports; trust
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3089546

With the wide popularity of various sport items and the continuous prosperity of sport business, player transfer events are becoming more and more frequent, which gives birth to a big player transfer market with attractive attentions of spectators. However, for the purposes of business competition or concerns of privacy disclosure, the multiple parties involved in an identical player transfer event are often reluctant to release the concrete transfer fees of a player as the transfer fee is always sensitive enough for the player himself or herself as well as the involved multiple sport clubs. The hidden player transfer fees block the fair and transparent sport business trades and cheat prospective spectators to some extent, which decreases the spectator trust towards player market value and finally, lowers the spectators' satisfaction towards sport business. In this situation, it is becoming a necessity to develop a trusted player transfer evaluation method supported by all the parties involved in player transfer events. Inspired by this challenging task, a trusted player transfer evaluation method with privacy protection is put forward in this paper, which is mainly based on Locality-Sensitive Hashing (LSH) and Blockchain technologies and named TPTE LSH+B . At last, a series of simulated experiments are deployed to validate the feasibility of our proposed TPTE LSH+B algorithm. Experimental results report that TPTE LSH+B has a good evaluation performance with competitive approaches.