6 citations · 15 across the 5 of their papers we have counts for
5 papers
Obsidian: Cooperative State-Space Exploration for Performant Inference on Secure ML Accelerators
Sarbartha Banerjee, Shijia Wei, Prakash Ramrakhyani +1
Trusted execution environments (TEEs) for machine learning accelerators are indispensable in secure and efficient ML inference. Optimizing workloads through state-space exploration…
SecDDR: Enabling Low-Cost Secure Memories by Protecting the DDR Interface
Ali Fakhrzadehgan, Prakash Ramrakhyani, Moinuddin K. Qureshi +1
The security goals of cloud providers and users include memory confidentiality and integrity, which requires implementing Replay-Attack protection (RAP). RAP can be achieved using…
Bandwidth Utilization Side-Channel on ML Inference Accelerators
Sarbartha Banerjee, Shijia Wei, Prakash Ramrakhyani +1
Accelerators used for machine learning (ML) inference provide great performance benefits over CPUs. Securing confidential model in inference against off-chip side-channel attacks i…
SESAME: Software defined Enclaves to Secure Inference Accelerators with Multi-tenant Execution
Sarbartha Banerjee, Prakash Ramrakhyani, Shijia Wei +1
Hardware-enclaves that target complex CPU designs compromise both security and performance. Programs have little control over micro-architecture, which leads to side-channel leaks,…
Shredder: Learning Noise Distributions to Protect Inference Privacy
Fatemehsadat Mireshghallah, Mohammadkazem Taram, Prakash Ramrakhyani +2
A wide variety of deep neural applications increasingly rely on the cloud to perform their compute-heavy inference. This common practice requires sending private and privileged dat…