Bitcoin Address Proximity Estimation

This project aimed at providing means to compute how likely it is that two bitcoin addresses belong to the same entity.

For this project, the client wanted to be able to estimate if two bitcoin addresses were used by the same person or entity. The client delivered several thousands of bitcoin transactions hashes with addresses used by their customers.

I developed a graph data model of the publically available bitcoin raw data to facilitate such analysis. Then, I enhanced the publically available bitcoin data with the data provided by the client. With such integrated data, I developed a set of real-time algorithms to compute the address proximity for any two addresses.

The real-time computations were exposed via a REST API in GoLang and visualized on custom frontend dashboards.

Data Scientist | Machine Learning Engineer | AI Advisor

20 years of experience in data processing from BigData to AI