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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Towards Real-time Voice Interaction Data Collection Monitoring and Ambient...

Tu Le (University of California, Irvine), Zixin Wang (Zhejiang University), Danny Yuxing Huang (New York University), Yaxing Yao (Virginia Tech), Yuan Tian (University of California, Los Angeles)

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A Comparison of Three Approaches to Assist Users in...

Michael Clark (Brigham Young University), Scott Ruoti (The University of Tennessee), Michael Mendoza (Imperial College London), Kent Seamons (Brigham Young University)

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Beyond the Bytes: Understanding the Limitations of Intrinsic Binary...

Peter Lafosse (Owner and Co-Founder of Vector 35 Inc.)

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WIP: A Trust Assessment Method for In-Vehicular Networks using...

Artur Hermann, Natasa Trkulja (Ulm University - Institute of Distributed Systems), Anderson Ramon Ferraz de Lucena, Alexander Kiening (DENSO AUTOMOTIVE Deutschland GmbH), Ana Petrovska (Huawei Technologies), Frank Kargl (Ulm University - Institute of Distributed Systems)

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