Paper:
Deanonymization in the Bitcoin P2P Network
Recent attacks on Bitcoin's peer-to-peer (P2P) network demonstrated that its transaction-flooding protocols, which are used to ensure network consistency, may enable user deanonymization-the linkage of a user's IP address with her pseudonym in the Bitcoin network. In 2015, the Bitcoin community responded to these attacks by changing the network's flooding mechanism to a different protocol, known as diffusion. However, it is unclear if diffusion actually improves the system's anonymity. In this paper, we model the Bitcoin networking stack and analyze its anonymity properties, both pre-and post-2015. The core problem is one of epidemic source inference over graphs, where the observational model and spreading mechanisms are informed by Bitcoin's implementation; notably, these models have not been studied in the epidemic source detection literature before. We identify and analyze near-optimal source estimators. This analysis suggests that Bitcoin's networking protocols (both pre-and post-2015) offer poor anonymity properties on networks with a regular-tree topology. We confirm this claim in simulation on a 2015 snapshot of the real Bitcoin P2P network topology.

Review:
The paper explores the effect of modifying the transaction-flooding protocol of the bitcoin network as a response to deanonymisation attacks in the original pre-2015 protocol. The authors model the original protocol (trickle) and the revised variant (diffusion) and suggest that deanonymisation can equally be established with both protocols. The authors further support their claim by exploring both models in simulations. 
Quality of Presentation: 
The paper is well-written (minor aspects: some incomplete sentences, unresolved reference) and provides the reader with the necessary background information to follow. 
The representation of the transaction-flooding protocols is comprehensive and introduced assumptions are highlighted.
Content-related questions:
- The authors refer to probability of detecting participants of 30%, which is relatively low (but indeed considerable). A fundamental assumption of performing the deanonymisation, however, is that the listening supernode would connect to all active bitcoin nodes. How realistic would it be in the future (assuming further growth of the network) that a supernode would be able to do so? -- Edit: The argument for the use of botnets made by the authors is a convincing counterpoint.
- While the overall work is involved, I find that the paper does not comprehensively answer the original claim that both trickle and diffusion perform equally good or bad at maintaining anonymity. Observing Figure 6, we can find that Trickle and Diffusion simulated show differences in probability of around 0.1 (with Diffusion performing worse than Trickle), which is not negligible. Figure 7 shows the operation on a snapshot of the real network, with an inverted observation: Diffusion performs better than Trickle. (Admittedly, they converge for larger number of eavesdropper connections.) The discrepancy and conflicting observations are worth discussing, and the statement that both protocols have `similar probabilities of detection' needs to be clarified (How do you establish 'similarity'?).