@article {82230,
	title = {Lightweight deep learning model to secure authentication in Mobile Cloud Computing},
	journal = {Journal of King Saud University-Computer and Information Sciences},
	volume = {34},
	number = {9},
	year = {2022},
	pages = {6938-6948},
	abstract = {The present paper suggests a\&nbsp;hybrid solution\&nbsp;to address two\&nbsp;key authentication\&nbsp;challenges: data privacy and the limitations of mobile devices resources. The former is addressed using Partially\&nbsp;Homomorphic Encryption\&nbsp;based on Paillier Algorithm for the encryption. While the latter is handled using a combination of\&nbsp;Deep Convolutional Neural Network\&nbsp;and Local Ternary Pattern for face recognition. We compare the accuracy and performance of our proposed solution to others proposed by the literature on ORL dataset and Extended Yale data set. Our findings suggest our proposed methods return higher recognition rates including 98.75\% on encrypted ORL data set and 98.78\% on encrypted Extended Yale data. In contrast, the existing methods achieved lower recognition rates, which have achieved only 92.50\% and 95.44\% of recognition rates on encrypted ORL and Extended Yale data set, respectively.},
	url = {https://doi.org/10.1016/j.jksuci.2021.09.016},
	author = {Abdelhakim Zeroual and Mohamed Amroune and Derdour Makhlouf and Atef Bentahar}
}
