eNNclave
Alexander Schlögl, Rainer Böhme
Abstract
Outsourcing machine learning inference creates a confidentiality dilemma: either the client has to trust the server with potentially sensitive input data, or the server has to share his commercially valuable model. Known remedies include homomorphic encryption, multi-party computation, or placing the entire model in a trusted enclave. None of these are suitable for large models. For two relevant use cases, we show that it is possible to keep all confidential model parameters in the last (dense) layers of deep neural networks. This allows us to split the model such that the confidential parts fit into a trusted enclave on the client side. We present the eNNclave toolchain to cut TensorFlow models at any layer, splitting them into public and enclaved layers. This preserves TensorFlow's performance optimizations and hardware support for public layers, while keeping the parameters of the enclaved layers private. Evaluations on several machine learning tasks spanning multiple domains show that fast inference is possible while keeping the sensitive model parameters confidential. Accuracy results are close to the baseline where all layers carry sensitive information and confirm our approach is practical.