CL-Shield: A Continuous Learning System for Protecting User Privacy

Abstract

The video analytics system utilizes deep learning models (DNN) to perform inference on the videos captured by cameras. Continuous learning algorithms are used to address the data drift problem in video analytics systems. However, uploading images from deployment environments and processing on the cloud carry the risk of privacy leakage. In this paper, we have designed a system called CL-Shield to protect user’s privacy. First, we review the causes of privacy leakage in a continuous learning system and propose the objective of full privacy protection. Second, we design an online training mechanism based on a scene library to avoid direct uploading of user’s frames to the cloud server. Lastly, we design a fast training set search algorithm based on a novel Ebv-List, which effectively improves the speed of model updates. We collect various real-world scenario data to build our scene library and validate our system on a dataset of over 10 hours. The experiments demonstrate that our privacy-aware continuous learning system achieves an F1-score of over 92% compared to the conventional systems without protecting privacy and has long-term stability in analytic F1-score.