SMU Data Science Review
Abstract
Trust is paramount for the effective operation of any monetary system. While the distributed architecture of blockchain technology on which cryptocurrencies operate has many benefits, the anonymity of users on the blockchain has provided criminal users an opportunity to hide both their identities and illicit activities. In this paper, we present a scoring mechanism for cryptocurrency users where the scores represent users’ trustworthiness as safe or risky transactors in the cryptocurrency community. In order to distinguish law-abiding users from potential threats in the Bitcoin marketplace, we analyze historical thefts to profile transactions, classify them into risky and non-risky categories using several machine learning techniques, and finally calculate a reputation score for every unique user based on their past association with any unlawful Bitcoin incident. The Support Vector Machine model based on two key attributes produces an accuracy of 86% and is considered the most applicable for our dataset. Our reputation score ranges from 0 to the total number of transactions by a given user where a higher score indicates greater trustworthiness in making Bitcoin transactions. This score helps to identify reputable users and, therefore, acts as a guideline for safe Bitcoin transactions. In the cryptocurrency marketplace, our self-attestation metric in the form of a reputation score offers a foundation for enhancing trust between transacting parties.
Recommended Citation
Freeman, Dan; McWilliams, Tim; Bhattacharyya, Sudip; Hall, Craig; and Peillard, Pablo
(2018)
"Enhancing Trust in the Cryptocurrency Marketplace: A Reputation Scoring Approach,"
SMU Data Science Review: Vol. 1:
No.
3, Article 5.
Available at:
https://scholar.smu.edu/datasciencereview/vol1/iss3/5
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
