Secure Face Recognition In Edge And Cloud Networks: From The Ensemble Learning Perspective

Offloading the computationally intensive workloads to the edge and cloud not only improves the quality of computation, but also creates an extra degree of diversity by collecting information from devices in service, which, in turn, has raised significant concerns on privacy as the aggregated information could be misused without the permission by the third party. Sparse coding, which has been successful in computer vision, is finding application in this new domain. In this paper, we develop a secure face recognition framework to orchestrate sparse coding in edge and cloud networks. Specifically, 1). To protect the privacy, we develop a low-complexity encrypting algorithm based on random unitary transform, where its influence on dictionary learning and sparse representation is analysed. We further prove that such influence will not affect the accuracy of face recognition. 2). To fully utilize the multi-device diversity, we extract deeper features in an intermediate space, expanded according to the dictionaries from each device, and perform classification in this new feature space to combat the noise and modeling error.