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Your Outer Appearance Mirrors Your Inner Self: Exploiting Unobservable Node Internals to Deanonymize Uploaders in Freenet

Yonghuan Xu, Ming Yang, Shan Wang, Xiaodan Gu, Zixia Liu, Zhen Ling

2026Year

Abstract

Freenet is a widely used anonymous communication system designed for file sharing. It preserves anonymity for both uploaders and downloaders via hop-by-hop routing and the enforcement of uniform protocols across nodes, preventing identification of the originating node along the routing path. Previous work has shown that the originating node can be deanonymized based on observable differences in interaction behaviors between nodes, while overlooking unobservable internal differences. In this paper, we identify a fundamental distinction between uploaders and relay nodes in their patterns of inserting application-layer messages into in-memory message queues. Although this difference is unobservable to a malicious node, we show that the FIFO (First-In, First-Out) property of the message queue allows the internal message insertion state to be mirrored in transmitted messages by triggering a beacon message. This insight enables a novel deanonymization attack against uploaders. We further address two challenges in conducting the attack: preventing the internal difference from being undermined and ensuring that it is effectively mirrored in transmitted messages. Real-world experiments demonstrate the feasibility and effectiveness of our attack, achieving a nearly 100% true positive rate with a maximum 4.17% false positive rate. Our work demonstrates that even unobservable internal differences can be potential threats to Freenet.

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