Deep-learning (DL) is receiving huge attention as enabling techniques for emerging mobile and IoT applications. It is a common practice to conduct DNN model-based inference using cloud services due to their high computation and memory cost. However, such a cloud-offloaded inference raises serious privacy concerns. Malicious external attackers or untrustworthy internal administrators of clouds may leak highly sensitive and private data such as image, voice and textual data. In this paper, we propose Occlumency, a novel cloud-driven solution designed to protect user privacy without compromising the benefit of using powerful cloud resources. Occlumency leverages secure SGX enclave to preserve the confidentiality and the integrity of user data throughout the entire DL inference process. DL inference in SGX enclave, however, impose a severe performance degradation due to limited physical memory space and inefficient page swapping. We designed a suite of novel techniques to accelerate DL inference inside the enclave with a limited memory size and implemented Occlumency based on Caffe. Our experiment with various DNN models shows that Occlumency improves inference speed by 3.6x compared to the baseline DL inference in SGX and achieves a secure DL inference within 72% of latency overhead compared to inference in the native environment.

IoT has long been a hot topic, and many researchers have made great efforts especially in wireless charging and communication between smart furniture, recognization and localization. However, people always divide charging, communicating and sensing apart. In our paper, we put forward a simple but practical method to make use of NFC communication and resonance-type WPT to build up a system which can be used to locate, identify, charge devices while providing a platform for objects to communicate with each other. Our system acts as an intelligent controller, and it links all devices registered to it together to form a scalable network based on common furniture in daily life.