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Yonghuan Xu

dblp:383/4929 · DBLP profile ↗
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2ranked-venue papers
2as first author
2since 2021 · last 2026
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 2 · 2 first-author · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
2 papers
Privacy and data protection · 58% Network security · 42%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Network security
anonymity networks
1.822026
Your Outer Appearance Mirrors Your Inner Self: Exploiting Unobservable Node Internals to Deanonymize Uploaders in Freenet · INFOCOM 2026
A De-anonymization Attack against Downloaders in Freenet · INFOCOM 2024
Privacy and data protection
de-anonymization
1.012026
Your Outer Appearance Mirrors Your Inner Self: Exploiting Unobservable Node Internals to Deanonymize Uploaders in Freenet · INFOCOM 2026
Privacy and data protection
information leakage
1.012026
Your Outer Appearance Mirrors Your Inner Self: Exploiting Unobservable Node Internals to Deanonymize Uploaders in Freenet · INFOCOM 2026
Privacy and data protection › de-anonymization
de-anonymization attack
0.812024
A De-anonymization Attack against Downloaders in Freenet · INFOCOM 2024
Network security
traffic analysis
0.212024
A De-anonymization Attack against Downloaders in Freenet · INFOCOM 2024

Methods — techniques the papers use, named apart from their topics

timeout manipulation · 0.8path folding analysis · 0.8
YearPublicationVenuePosition
2026 Your Outer Appearance Mirrors Your Inner Self: Exploiting Unobservable Node Internals to Deanonymize Uploaders in Freenet
Yonghuan Xu, Ming Yang 0001, Shan Wang 0008, Xiaodan Gu, Zixia Liu, Zhen Ling 0001
INFOCOM1
2024 A De-anonymization Attack against Downloaders in Freenet
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.
Yonghuan Xu, Ming Yang 0001, Zhen Ling 0001, Zixia Liu, Xiaodan Gu
INFOCOM1