A De-anonymization Attack against Downloaders in Freenet
Yonghuan Xu, Ming Yang, Zhen Ling, Zixia Liu, Xiaodan Gu, Lan Luo
Abstract
Freenet is a well-known anonymous communication system that enables file sharing among users. It employs a probabilistic hops-to-live (HTL) decrement approach to hide the originator among nodes in a multi-hop path. Therefore, all nodes shall exhibit identical behaviors to preserve anonymity. However, we discover that the path folding mechanism in Freenet violates this principle due to behavior discrepancy between downloaders and intermediate nodes. The path folding mechanism is designed to optimize the network topology of Freenet. A delayed path folding message by a successor node may incur a timeout event at its predecessor, and an intermediate node reacts differently to such timeout with a downloader. Therefore, malicious nodes can deliberately trigger the timeout event to identify downloaders. The complex implementation of the path folding timeout detection mechanism in Freenet complicates our de-anonymization attack. We thoroughly analyze the underlying cause and develop three strategies to manipulate three types of messages respectively at the malicious node, minimizing the false positive rate. We conduct extensive real-world experiments to verify the feasibility and effectiveness of our attack. They show that our attack achieves a true positive rate of 100% and false positive rate of near 0% under two different Freenet download modes.
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Builds on2
- DeepCorr: Strong Flow Correlation Attacks on Tor Using Deep LearningMilad Nasr, Alireza Bahramali, Amir HoumansadrCCS 2018 · 187 citations
- A Forensically Sound Method of Identifying Downloaders and Uploaders in FreenetBrian Neil Levine, Marc Liberatore, Brian Lynn, Matthew WrightCCS 2020 · 5 citations
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