A two‐stage privacy protection mechanism based on blockchain in mobile crowdsourcing

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 TPTELSH+B. At last, a series of simulated experiments are deployed to validate the feasibility of our proposed TPTELSH+B algorithm. Experimental results report that TPTELSH+B has a good evaluation performance with competitive approaches.

Section: Discussionmentioning

confidence: 96%

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

Liu

et al. 2021

“…In future study, we will continuously refine SIFSimHash by considering more data types [25][26][27][28][29] and more optimization metrics [30][31][32][33][34][35]. Besides, how to integrate SIFSimHash with other privacy protection techniques (e.g., in [36][37][38][39][40][41][42][43][44][45]) is another research focus.…”

Section: Discussionmentioning

confidence: 99%

A Novel Similar Player Clustering Method With Privacy Preservation for Sport Performance Evaluation in Cloud

Xing³

et al. 2021

With the ever-increasing popularity of sports and health ideas, people are paying more attentions to gaining high-quality healthy life through various taking various sport items or exercises. Through observing and analyzing the past sport exercise score records, we can cluster the players into different categories, each of which share the same or similar sport preferences or performances. However, the sport exercise score records are often massive and often stored in different cloud platforms, which raise a big difficulty for time-efficient player clustering. Furthermore, the sport exercise score records are a kind of privacy for most players; therefore, it is often not rational or legal to release these sensitive data to the public for similar player clustering purpose. Considering the above two issues, we use SimHash, a kind of privacy-aware approximate neighbor search technique, for similar player clustering by analyzing the sport exercise score records distributed across different cloud platforms. Thus, we can realize privacy-aware similar player clustering through SimHash. At last, we provide a set of experiments to validate the advantages of our proposed privacy-aware similar player clustering algorithm. Reported experimental results show the effectiveness of our proposal in remedying the big data volume and privacy concerns in player clustering based on sport exercise score records.

“…Blockchain is considered a technology that may provide a powerful solution to network security and a high level of privacy protection [82]. Such as encryption and signature algorithm [83], k-anonymity [84] and transparency [85] of Blockchain can effectively protect privacy. Data security and privacy protection are challenges to be accepted in the explosive growth of data and rapid development of technology.…”

Section: ) Cluster 1 (Red): Data Security and Privacy Protectionmentioning

confidence: 99%

Bibliometric Analysis of the Blockchain Scientific Evolution: 2014–2020

Luo

Bai

2021

As one of the disruptive technologies in the fourth industrial revolution, Blockchain has irrevocably changed how people live and work. Many scholars have studied the Blockchain, and the number of related publications has increased over time. Bibliometrics is an excellent method to study the development trend of a specific field. However, few scholars have used bibliometrics to research Blockchain; the inheritance and evolution among its different research themes have not yet been fully understood. This study emphasizes the scientific evolution and research focus of Blockchain through bibliometric analysis. We analyzed 2993 publications from 2014 to 2020 using co-word analysis and cluster analysis with bibliometric tools such as the SciMAT. The most productive author, country, institutions and leading research areas were identified, outlining the Blockchain field's fundamental developments. The results show the changes in keywords regarding Blockchain summarize the research hotspots in different periods. The study discusses the evolution process of various themes in each period, providing a meaningful reference for scholars to identify research hotspots and future research directions in the Blockchain field.