As Video Streaming and Analytics (VSA) systems become increasingly popular, serious privacy concerns have risen on exposing too much unnecessary private information to the VSA providers. Yet, it is challenging to protect privacy while still preserving desired VSA features, i.e., effective analytics, forensic support, resource efficiency, and real-time execution. In this paper, we present a VSA privacy enhancement system (PECAM), which addresses the above challenge with no change in the VSA back-end. PECAM leverages a novel Generative Adversarial Network to perform the privacy-enhanced securely-reversible video transformation. PECAM also incorporates a couple of system optimizations into its VSA workflow to reduce network bandwidth usage and enable real-time processing on cameras. We implement our PECAM prototype on commodity hardware and evaluate its performance via both security study and extensive experiments. Results demonstrate that PECAM can effectively enhance the visual privacy of VSA in the presence of an adversary, and its transformed videos, when taken as input for various VSA back-end tasks, maintain a 96% accuracy of corresponding original videos. Additionally, it performs 12.3× and 1.8× better than baseline methods in terms of the computing cost and network bandwidth usage, respectively.

The data abuse issue has risen along with the widespread development of the deep learning inference service (DLIS). Specifically, mobile users worry about their input data being labeled to secretly train new deep learning models that are unrelated to the DLIS they subscribe to. This unique issue, unlike the privacy problem, is about the rights of data owners in the context of deep learning. However, preventing data abuse is demanding when considering the usability and generality in the mobile scenario. In this work, we propose, to our best knowledge, the first data abuse prevention mechanism called DAPter. DAPter is a user-side DLIS-input converter, which removes unnecessary information with respect to the targeted DLIS. The converted input data by DAPter maintains good inference accuracy and is difficult to be labeled manually or automatically for the new model training. DAPter’s conversion is empowered by our lightweight generative model trained with a novel loss function to minimize abusable information in the input data. Furthermore, adapting DAPter requires no change in the existing DLIS backend and models. We conduct comprehensive experiments with our DAPter prototype on mobile devices and demonstrate that DAPter can substantially raise the bar of the data abuse difficulty with little impact on the service quality and overhead.

Real-time user emotion recognition is highly desirable for many applications on eyewear devices like smart glasses. However, it is very challenging to enable this capability on such devices due to tightly constrained image contents (only eye-area images available from the on-device eye-tracking camera) and computing resources of the embedded system. In this paper, we propose and develop a novel system called EMO that can recognize, on top of a resource-limited eyewear device, real-time emotions of the user who wears it. Unlike most existing solutions that require whole-face images to recognize emotions, EMO only utilizes the single-eye-area images captured by the eye-tracking camera of the eyewear. To achieve this, we design a customized deep-learning network to effectively extract emotional features from input single-eye images and a personalized feature classifier to accurately identify a user's emotions. EMO also exploits the temporal locality and feature similarity among consecutive video frames of the eye-tracking camera to further reduce the recognition latency and system resource usage. We implement EMO on two hardware platforms and conduct comprehensive experimental evaluations. Our results demonstrate that EMO can continuously recognize seven-type emotions at 12.8 frames per second with a mean accuracy of 72.2%, significantly outperforming the state-of-the-art approach, and consume much fewer system resources.