Diwen Xue (University of Michigan), Robert Stanley (University of Michigan), Piyush Kumar (University of Michigan), Roya Ensafi (University of Michigan)

The escalating global trend of Internet censorship has necessitated an increased adoption of proxy tools, especially obfuscated circumvention proxies. These proxies serve a fundamental need for access and connectivity among millions in heavily censored regions. However, as the use of proxies expands, so do censors' dedicated efforts to detect and disrupt such circumvention traffic to enforce their information control policies.

In this paper, we bring out the presence of an inherent fingerprint for detecting obfuscated proxy traffic. The fingerprint is created by the misalignment of transport- and application-layer sessions in proxy routing, which is reflected in the discrepancy in Round Trip Times (RTTs) across network layers. Importantly, being protocol-agnostic, the fingerprint enables an adversary to effectively target multiple proxy protocols simultaneously. We conduct an extensive evaluation using both controlled testbeds and real-world traffic, collected from a partner ISP, to assess the fingerprint's potential for exploitation by censors. In addition to being of interest on its own, our timing-based fingerprinting vulnerability highlights the deficiencies in existing obfuscation approaches. We hope our study brings the attention of the circumvention community to packet timing as an area of concern and leads to the development of more sustainable countermeasures.

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A Multifaceted Study on the Use of TLS and...

Ka Fun Tang (The Chinese University of Hong Kong), Che Wei Tu (The Chinese University of Hong Kong), Sui Ling Angela Mak (The Chinese University of Hong Kong), Sze Yiu Chau (The Chinese University of Hong Kong)

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DShield: Defending against Backdoor Attacks on Graph Neural Networks...

Hao Yu (National University of Defense Technology), Chuan Ma (Chongqing University), Xinhang Wan (National University of Defense Technology), Jun Wang (National University of Defense Technology), Tao Xiang (Chongqing University), Meng Shen (Beijing Institute of Technology, Beijing, China), Xinwang Liu (National University of Defense Technology)

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BumbleBee: Secure Two-party Inference Framework for Large Transformers

Wen-jie Lu (Ant Group), Zhicong Huang (Ant Group), Zhen Gu (Alibaba Group), Jingyu Li (Ant Group & Zhejiang University), Jian Liu (Zhejiang University), Cheng Hong (Ant Group), Kui Ren (Zhejiang University), Tao Wei (Ant Group), WenGuang Chen (Ant Group)

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