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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Compensating Removed Frequency Components: Thwarting Voice Spectrum Reduction Attacks

Shu Wang (George Mason University), Kun Sun (George Mason University), Qi Li (Tsinghua University)

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CAN-MIRGU: A Comprehensive CAN Bus Attack Dataset from Moving...

Sampath Rajapaksha, Harsha Kalutarage (Robert Gordon University, UK), Garikayi Madzudzo (Horiba Mira Ltd, UK), Andrei Petrovski (Robert Gordon University, UK), M.Omar Al-Kadri (University of Doha for Science and Technology)

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ActiveDaemon: Unconscious DNN Dormancy and Waking Up via User-specific...

Ge Ren (Shanghai Jiao Tong University), Gaolei Li (Shanghai Jiao Tong University), Shenghong Li (Shanghai Jiao Tong University), Libo Chen (Shanghai Jiao Tong University), Kui Ren (Zhejiang University)

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