January 2018
The Bitfury Group Unveils Solution For Analyzing Related Bitcoin Addresses
The Bitfury Group, the world's leading full-service Blockchain technology company detailed a new solution to help reveal relationships between bitcoin addresses that minimizes errors in data and ensures greater accuracy in linking bitcoin addresses for criminal investigations, while also showing bitcoin users ways to protect their privacy.
"Currently, bitcoin users can have multiple addresses, making it easier to conceal identities and commit crimes on the Blockchain. The ability to link related addresses, called 'clustering,' is an important new tool that helps law enforcement agencies conduct criminal investigations," said Valery Vavilov, CEO of The Bitfury Group.
Bitcoin address clustering is a process that exposes bitcoin users by determining which addresses belong to a single user through an analysis of Blockchain data. The act of clustering groups those addresses together, enabling investigators to link them to a single entity.
In a new report, Bitfury reveals a comprehensive approach to clustering that reduces errors in results. While prior algorithms constructed clustering models using Blockchain information and validated it with off-chain data, such as public information on the Internet, Bitfury’s new method uses both data types during the model construction step. The proposed approach allows investigators to reduce errors in unreliable input data sources and achieve a higher level of accuracy.
To see the algorithms developed by the Bitfury team, read the full white paper here.
Bitcoin address clustering is a process that exposes bitcoin users by determining which addresses belong to a single user through an analysis of Blockchain data. The act of clustering groups those addresses together, enabling investigators to link them to a single entity.
In a new report, Bitfury reveals a comprehensive approach to clustering that reduces errors in results. While prior algorithms constructed clustering models using Blockchain information and validated it with off-chain data, such as public information on the Internet, Bitfury’s new method uses both data types during the model construction step. The proposed approach allows investigators to reduce errors in unreliable input data sources and achieve a higher level of accuracy.
To see the algorithms developed by the Bitfury team, read the full white paper here.