Poster: Cryptographic Inferences for Video Deep Neural Networks

Abstract

Deep neural network (DNN) services have been widely deployed in many different domains. For instance, a client may send its private input data (e.g., images, texts and videos) to the cloud for accurate inferences with pre-trained DNN models. However, significant privacy concerns would emerge in such applications due to the potential data or model sharing. Secure inferences with cryptographic techniques have been proposed to address such issues, and the system can perform secure two-party inferences between each client and cloud. However, most of existing cryptographic systems only focus on DNNs for extracting 2D features for image inferences, which have major limitations on latency and scalability for extracting spatio-temporal (3D) features from videos for accurate inferences. To address such critical deficiencies, we design and implement the first cryptographic inference system, Crypto3D, which privately infers videos on 3D features with rigorous privacy guarantees. We evaluate Crypto3D and benchmark with the state-of-the-art systems on privately inferring videos in the UCF-101 and HMDB-51 datasets with C3D and I3D models. Our results demonstrate that Crypto3D significantly outperforms existing systems (substantially extended to inferences with 3D features): execution time: 186.89x vs. CryptoDL (3D), 63.75x vs. HEANN (3D), 61.52x vs. MP-SPDZ (3D), 45x vs. E2DM (3D), 3.74x vs. Intel SGX (3D), and 3x vs. Gazelle (3D); accuracy: 82.3% vs. below 70% for all of them.