AegisDNN: Dependable and Timely Execution of DNN Tasks with SGX

Abstract

With the rising demand for emerging DNN applications in safety-critical systems, much attention has been given to the reliability and trustworthiness of DNN inference output against malicious attacks. Although prior work has been conducted to improve the privacy of DNN inference by executing the entire DNN model inside Intel SGX enclaves, existing approaches pose severe performance challenges to achieve dependable and timely execution simultaneously. In this paper, we propose AegisDNN, a DNN inference framework to address this problem. AegisDNN leverages secure SGX enclaves for protecting only the critical part of real-time DNN tasks which are vulnerable to potential fault injection attacks. To choose the right set of layers for protection while ensuring the timeliness of task execution, AegisDNN includes a dynamic-programming based algorithm that finds a layer protection configuration for each task to meet the real-time and dependability requirements based on the layer-wise DNN time and SDC (Silent Data Corruption) profiling mechanism. AegisDNN also utilizes a machine-learning based SDC prediction method to significantly reduce the time for estimating SDC rates for all possible layer protection configurations. We implemented AegisDNN on Caffe, PyTorch, and Tensorflow with Eigen BLAS ported into SGX enclaves to comprehensively demonstrate the effectiveness of AegisDNN against state-of-the-art DNN fault-injection attacks. Experiment results indicate that AegisDNN could satisfy both dependability and real-time requirements simultaneously, when none of the other compared approaches could do so.