<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Abdelhakim Zeroual</style></author><author><style face="normal" font="default" size="100%">Mohamed Amroune</style></author><author><style face="normal" font="default" size="100%">Derdour Makhlouf</style></author><author><style face="normal" font="default" size="100%">Atef Bentahar</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Lightweight deep learning model to secure authentication in Mobile Cloud Computing</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of King Saud University-Computer and Information Sciences</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1016/j.jksuci.2021.09.016</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">34</style></volume><pages><style face="normal" font="default" size="100%">6938-6948</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The present paper suggests a&amp;nbsp;&lt;a href=&quot;https://www.sciencedirect.com/topics/computer-science/hybrid-solution&quot; title=&quot;Learn more about hybrid solution from ScienceDirect's AI-generated Topic Pages&quot;&gt;hybrid solution&lt;/a&gt;&amp;nbsp;to address two&amp;nbsp;&lt;a href=&quot;https://www.sciencedirect.com/topics/computer-science/key-authentication&quot; title=&quot;Learn more about key authentication from ScienceDirect's AI-generated Topic Pages&quot;&gt;key authentication&lt;/a&gt;&amp;nbsp;challenges: data privacy and the limitations of mobile devices resources. The former is addressed using Partially&amp;nbsp;&lt;a href=&quot;https://www.sciencedirect.com/topics/computer-science/homomorphic-encryption&quot; title=&quot;Learn more about Homomorphic Encryption from ScienceDirect's AI-generated Topic Pages&quot;&gt;Homomorphic Encryption&lt;/a&gt;&amp;nbsp;based on Paillier Algorithm for the encryption. While the latter is handled using a combination of&amp;nbsp;&lt;a href=&quot;https://www.sciencedirect.com/topics/computer-science/deep-convolutional-neural-networks&quot; title=&quot;Learn more about Deep Convolutional Neural Network from ScienceDirect's AI-generated Topic Pages&quot;&gt;Deep Convolutional Neural Network&lt;/a&gt;&amp;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.</style></abstract><issue><style face="normal" font="default" size="100%">9</style></issue></record></records></xml>