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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Threats Against Satellite Ground Infrastructure: A retrospective analysis of...

Jessie Hamill-Stewart (University of Bristol and University of Bath), Awais Rashid (University of Bristol)

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SENSE: Enhancing Microarchitectural Awareness for TEEs via Subscription-Based Notification

Fan Sang (Georgia Institute of Technology), Jaehyuk Lee (Georgia Institute of Technology), Xiaokuan Zhang (George Mason University), Meng Xu (University of Waterloo), Scott Constable (Intel), Yuan Xiao (Intel), Michael Steiner (Intel), Mona Vij (Intel), Taesoo Kim (Georgia Institute of Technology)

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Securing the Satellite Software Stack

Samuel Jero (MIT Lincoln Laboratory), Juliana Furgala (MIT Lincoln Laboratory), Max A Heller (MIT Lincoln Laboratory), Benjamin Nahill (MIT Lincoln Laboratory), Samuel Mergendahl (MIT Lincoln Laboratory), Richard Skowyra (MIT Lincoln Laboratory)

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Efficient and Timely Revocation of V2X Credentials

Gianluca Scopelliti (Ericsson & KU Leuven), Christoph Baumann (Ericsson), Fritz Alder (KU Leuven), Eddy Truyen (KU Leuven), Jan Tobias Mühlberg (Université libre de Bruxelles & KU Leuven)

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