SeSeMI shows that SGX enclaves can be reused and packed inside a standard serverless platform to serve encrypted model inference with lower latency and lower memory cost than existing TEE-serverless designs.
Serverless data science - are we there yet? a case study of model serving,
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SeSeMI: Secure Serverless Model Inference on Sensitive Data
SeSeMI shows that SGX enclaves can be reused and packed inside a standard serverless platform to serve encrypted model inference with lower latency and lower memory cost than existing TEE-serverless designs.