渗透框架揭示了阴谋，暗网和区块链网络中的隐私限制
Percolation framework reveals limits of privacy in Conspiracy, Dark Web, and Blockchain networks
摘要：
我们考虑网络中个人之间交互的隐私。对于许多网络，虽然节点对外部观察者是匿名的，但个体之间存在联系意味着一个节点可能会泄露其邻居的身份信息。此外，虽然账户的身份可能对观察者隐藏，但两个匿名账户之间的互动网络通常是可用的。例如，在区块链加密货币中，两个匿名账户之间的交易是公开发布的。在这里，我们考虑如果此类网络中的一方（或多方）被外部身份去匿名化会发生什么。这些受感染的个人可能会泄露与他们互动的其他人的信息，然后可能会级联到越来越多的节点信息被泄露。我们使用一个渗透框架来分析上述场景，并显示了不同可能性的个人拥有关于其交易对手的信息，可以识别的账户的比例以及从去匿名节点到匿名节点的理想化最小步骤数（去匿名化个人所需的工作量的度量）。我们进一步开发了一个贪婪的算法来估计\emph{actual}的步骤，将需要识别一个特定的节点的基础上提供给攻击者的嘈杂的信息。我们将我们的框架应用于三个现实世界的网络：（1）区块链交易网络，（2）暗网上的交互网络，以及（3）政治阴谋网络。我们发现，在所有三个网络中，从一个受损的个人开始，有可能在不到5步的时间内对网络的一个重要部分（$>50$ %）进行去匿名化。总的来说，这些结果为寻求识别匿名网络中的行为者的调查人员以及寻求维护隐私的用户提供了指导。
英文摘要：
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 and show for different likelihoods of individuals possessing information on their counter-parties, the fraction of accounts that can be identified and the idealized minimum number of steps from a deanonymized node to an anonymous node (a measure of the effort required to deanonymize that individual). We further develop a greedy algorithm to estimate the \emph{actual} number of steps that will be needed to identify a particular node based on the noisy information available to the attacker. We apply our framework to three real-world networks: (1) a blockchain transaction network, (2) a network of interactions on the dark web, and (3) a political conspiracy network. We find that in all three networks, beginning from one compromised individual, it is possible to deanonymize a significant fraction of the network ($>50$%) within less than 5 steps. Overall these results provide guidelines for investigators seeking to identify actors in anonymous networks, as well as for users seeking to maintain their privacy.
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