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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Connecting the Dots in the Sky: Website Fingerprinting in...

Prabhjot Singh (University of Waterloo), Diogo Barradas (University of Waterloo), Tariq Elahi (University of Edinburgh), Noura Limam (University of Waterloo)

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Wait, What Does a SOC Do?

Joe Nehila, Drew Walsh (Deloitte And Touche)

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Designing and Evaluating a Testbed for the Matter Protocol:...

Ravindra Mangar (Dartmouth College) Jingyu Qian (University of Illinois), Wondimu Zegeye (Morgan State University), Abdulrahman AlRabah, Ben Civjan, Shalni Sundram, Sam Yuan, Carl A. Gunter (University of Illinois), Mounib Khanafer (American University of Kuwait), Kevin Kornegay (Morgan State University), Timothy J. Pierson, David Kotz (Dartmouth College)

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Exploiting Sequence Number Leakage: TCP Hijacking in NAT-Enabled Wi-Fi...

Yuxiang Yang (Tsinghua University), Xuewei Feng (Tsinghua University), Qi Li (Tsinghua University), Kun Sun (George Mason University), Ziqiang Wang (Southeast University), Ke Xu (Tsinghua University)

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