Angelo Ruocco, Chris Porter, Claudio Carvalho, Daniele Buono, Derren Dunn, Hubertus Franke, James Bottomley, Marcio Silva, Mengmei Ye, Niteesh Dubey, Tobin Feldman-Fitzthum (IBM Research)

Developers leverage machine learning (ML) platforms to handle a range of their ML tasks in the cloud, but these use cases have not been deeply considered in the context of confidential computing. Confidential computing’s threat model treats the cloud provider as untrusted, so the user’s data in use (and certainly at rest) must be encrypted and integrity-protected. This host-guest barrier presents new challenges and opportunities in the ML platform space. In particular, we take a glancing look at ML platforms’ pipeline tools, how they currently align with the Confidential Containers project, and what may be needed to bridge several gaps.

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Proof of Backhaul: Trustfree Measurement of Broadband Bandwidth

Peiyao Sheng (Kaleidoscope Blockchain Inc.), Nikita Yadav (Indian Institute of Science), Vishal Sevani (Kaleidoscope Blockchain Inc.), Arun Babu (Kaleidoscope Blockchain Inc.), Anand Svr (Kaleidoscope Blockchain Inc.), Himanshu Tyagi (Indian Institute of Science), Pramod Viswanath (Kaleidoscope Blockchain Inc.)

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HistCAN: A real-time CAN IDS with enhanced historical traffic...

Shuguo Zhuo, Nuo Li, Kui Ren (The State Key Laboratory of Blockchain and Data Security, Zhejiang University)

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Low-Quality Training Data Only? A Robust Framework for Detecting...

Yuqi Qing (Tsinghua University), Qilei Yin (Zhongguancun Laboratory), Xinhao Deng (Tsinghua University), Yihao Chen (Tsinghua University), Zhuotao Liu (Tsinghua University), Kun Sun (George Mason University), Ke Xu (Tsinghua University), Jia Zhang (Tsinghua University), Qi Li (Tsinghua University)

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GTrans: Graph Transformer-Based Obfuscation-resilient Binary Code Similarity Detection

Yun Zhang (Hunan University), Yuling Liu (Hunan University), Ge Cheng (Xiangtan University), Bo Ou (Hunan University)

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