Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

6-2019

Abstract

The Bitcoin transaction graph is a public data structure organized as transactions between addresses, each associated with a logical entity. In this work, we introduce a complete probabilistic model of the Bitcoin Blockchain, setting the basis for follow-up AI applications on Bitcoin transactions. We first formulate a set of conditional dependencies induced by the Bitcoin protocol at the block level and derive a corresponding fully observed graphical model of a Bitcoin block. We then extend the model to include hidden entity attributes such as the functional category of the associated logical agent and derive asymptotic bounds on the privacy properties implied by this model. At the network level, we show evidence of complex transaction-to-transaction behavior and present a relevant discriminative model of the agent categories. Performance of both the block-based graphical model and the network-level discriminative model are evaluated on a subset of the public Bitcoin Blockchain.

Discipline

Numerical Analysis and Scientific Computing

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

Proceedings of the 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019, Long Beach, CA, USA, June 16-17

First Page

2784

Last Page

2792

ISBN

9781728125060

Identifier

10.1109/CVPRW.2019.00337

Publisher

IEEE Computer Society

City or Country

Los Alamitos, CA

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