EdgeMask: An Edge-based Privacy Preserving Service for Video Data Sharing

Abstract

Preserving privacy in image and video data captured from public environments is essential for any research group that leverages, publishes, or shares such data. Although there are several research efforts attempting to resolve the privacy issues, they had quality and efficiency limitations. In this work, we proposed EdgeMask as a privacy preserving service that leverages edge computing and deep learning models to propose a real-time object segmentation approach and analyze the input data using parallel computing and speed up the object removal. Our experimental results indicate that EdgeMask reduces the computational time considerably.