Combining Asynchronous Task Parallelism and Intel SGX for Secure Deep Learning : (Practical Experience Report)

A common way of improving performance of applications for multi-core processors is to exploit parallelism. In deep learning (DL), training or tuning parameters use user’s sensitive data, and thus preserving privacy is critical. Hardware-assisted protection mechanisms (i.e., trusted execution environments - TEEs) offer a practical privacy-preserving solution, nowadays available both in private and public data centers. We present SGX-OmpSs, a new approach combining a task-based programming model (i.e., OmpSs) with TEEs (i.e., Intel Software Guard Extensions). SGX-OmpSs supports asynchronous task parallelism and hardware heterogeneity by using the data dependencies between tasks of the application, easily specified by code annotations. We evaluate SGX-OmpSs via several microbenchmarks and state-of-the-art DL applications and datasets (e.g., YOLO and MNIST). SGX-OmpSs achieves 94% gain speedup while offering additional security guarantees.