Privacy preserving face recognition in cloud robotics: a comparative study

Chiranjeevi, Karri; Cheikhrouhou, Omar; Harbaoui, Ahmed; Zaguia, Atef; Hamam, Habib

doi:http://dx.doi.org/10.3390/app11146522

Privacy preserving face recognition in cloud robotics: a comparative study

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Chiranjeevi K, Cheikhrouhou O, Harbaoui A, Zaguia A, Hamam H. Privacy preserving face recognition in cloud robotics: a comparative study. Appl Sci (Basel). 2021 Jul 2;11(14):6522. DOI: 10.3390/app11146522

Abstract

Real-time robotic applications encounter the robot on board resources' limitations. The speed of robot face recognition can be improved by incorporating cloud technology. However, the transmission of data to the cloud servers exposes the data to security and privacy attacks. Therefore, encryption algorithms need to be set up. This paper aims to study the security and performance of potential encryption algorithms and their impact on the deep-learning-based face recognition task's accuracy. To this end, experiments are conducted for robot face recognition through various deep learning algorithms after encrypting the images of the ORL database using cryptography and image-processing based algorithms.