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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On the Realism of LiDAR Spoofing Attacks against Autonomous...

Takami Sato (University of California, Irvine), Ryo Suzuki (Keio University), Yuki Hayakawa (Keio University), Kazuma Ikeda (Keio University), Ozora Sako (Keio University), Rokuto Nagata (Keio University), Ryo Yoshida (Keio University), Qi Alfred Chen (University of California, Irvine), Kentaro Yoshioka (Keio University)

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Passive Inference Attacks on Split Learning via Adversarial Regularization

Xiaochen Zhu (National University of Singapore & Massachusetts Institute of Technology), Xinjian Luo (National University of Singapore & Mohamed bin Zayed University of Artificial Intelligence), Yuncheng Wu (Renmin University of China), Yangfan Jiang (National University of Singapore), Xiaokui Xiao (National University of Singapore), Beng Chin Ooi (National University of Singapore)

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Repurposing Neural Networks for Efficient Cryptographic Computation

Xin Jin (The Ohio State University), Shiqing Ma (University of Massachusetts Amherst), Zhiqiang Lin (The Ohio State University)

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