Percolation framework reveals limits of privacy in Conspiracy, Dark Web, and Blockchain networks
Louis M Shekhtman, Alon Sela, Shlomo Havlin

TL;DR
This paper uses a percolation framework to analyze how privacy can be compromised in various anonymous networks, revealing that a significant portion of nodes can be deanonymized rapidly once some individuals are compromised.
Contribution
It introduces a percolation-based model to quantify the limits of privacy in anonymous networks and develops an algorithm to estimate the effort needed to deanonymize nodes.
Findings
Over 50% of nodes can be deanonymized within 5 steps.
The framework applies to blockchain, dark web, and conspiracy networks.
Rapid deanonymization is possible from a single compromised individual.
Abstract
We consider the privacy of interactions between individuals in a network. For many networks, while nodes are anonymous to outside observers, the existence of a link between individuals implies the possibility of one node revealing identifying information about its neighbor. Moreover, while the identities of the accounts are likely hidden to an observer, the network of interaction between two anonymous accounts is often available. For example, in blockchain cryptocurrencies, transactions between two anonymous accounts are published openly. Here we consider what happens if one (or more) parties in such a network are deanonymized by an outside identity. These compromised individuals could leak information about others with whom they interacted, which could then cascade to more and more nodes' information being revealed. We use a percolation framework to analyze the scenario outlined above…
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Taxonomy
TopicsSpam and Phishing Detection · Internet Traffic Analysis and Secure E-voting · Blockchain Technology Applications and Security
