  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. 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 is evaluated on a subset of the public Bitcoin Blockchain.
