TY - JOUR
T1 - An efficient queries processing model based on Multi Broadcast Searchable Keywords Encryption (MBSKE)
AU - Al-Maytami, Belal Ali
AU - Fan, Pingzhi
AU - Hussain, Abir Jaafar
AU - Baker, Thar
AU - Liatsis, Panos
N1 - Funding Information:
This work was supported by the 111 Project under Grant No. 111-2-14. Belal Ali Al-Maytami received the MS degree in Computer Science from University of Pune, India, in 2010. He is currently working toward the Ph.D. degree with the Key Laboratory of Cloud Computing, School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China. His current research interests include computation, communication protocols and algorithm designs. Pingzhi Fan (M’93-SM’99-F’15) received Ph.D. degree in electronic engineering from the University of Hull, Yorkshire, U.K., in 1994. He is currently a professor and the Director of the Institute of Mobile Communications, Southwest Jiaotong University, Chengdu, China. He is the author of over 200 research papers published in various academic journals (in English) and eight books, as well as the holder of 20 granted patents. His research interests include high-mobility wireless communications, fifth-generation technologies, wireless networks for big data, and signal design and coding, etc. Dr. Fan has served as the General Chair or Technical Program Committee Chair of a number of international conferences and as the Guest Editor-in-Chief, Guest Editor, or Editorial Member of several international journals. He received the U.K. ORS Award, the NSFC Outstanding Young Scientist Award, and the position as Chief Scientist of a National 973 Research Project. He is the Founding Chair of the IEEE Vehicular Technology Society Beijing Chapter and the IEEE Communication Society Chengdu Chapter, as well as the Founding Chair of IEEE Chengdu Section. He also served as a Board Member of IEEE Region 10, The Institution of Engineering and Technology (IET, formerly Institution of Electrical Engineers) Council, and the IET Asia-Pacific Region. Abir Jaafar Hussain is a professor of Machine Learning and she is the head of the Applied Computing Research Group at the Faculty of Engineering and Technology at Liverpool John Moores University, UK. She completed her Ph.D. study at The University of Manchester, UK in 2000 with a thesis title Polynomial Neural Networks for Image and Signal Processing. She has published numerous referred research papers in conferences and Journal in the research areas of e-learning, Neural Networks, Signal Prediction, Telecommunication Fraud Detection and Image Compression. She is a Ph.D. supervisor and an external examiner for research degrees including Ph.D. and M.Phil. Dr Thar Baker is a Senior Lecturer in Distributed Systems and Head of Applied Computing Research Group (ACRG) in the Department of Computer Science at the Faculty of Engineering and Technology. He has received his Ph.D. in Autonomic Cloud Applications from LJMU in 2010, and became a Senior Fellow in Higher Education Academy (SFHEA) in 2018. Dr Baker has published numerous refereed research papers in multidisciplinary research areas including: Cloud Computing, Distributed Software Systems, Big Data, Algorithm Design, Green and Sustainable Computing, and Autonomic Web Science. He has been actively involved as a member of editorial board and review committee for a number of peer reviewed international journals, and is on programme committee for a number of international conferences. Dr. Baker was appointed as Expert Evaluator in the European FP7 Connected Communities CONFINE project (2012-2015). Dr Panos Liatsis is a Professor in the Department of Electrical Engineering and Computer Science at Khalifa University of Science and Technology. Prior to joining Khalifa University, he was Professor of Image Processing and Head of Department of Electrical and Electronic Engineering at City, University of London. He received the Diploma degree in Electrical Engineering from the University of Thrace in Greece and the PhD degree in Electrical Engineering & Electronics from the University of Manchester, UK. Dr Liatsis’ research interests include machine learning, pattern recognition, and computer vision, with applications in biomedical image and signal processing, security, and intelligent transportation systems. For the second author PingZhi Fan please update the email to [email protected]. The last updating is the Acknowledgment please change as : This work was supported by the 111 Project under Grant No.111-2-14.
Publisher Copyright:
© 2019
PY - 2020/3/1
Y1 - 2020/3/1
N2 - Cloud computing is a technology which has enabled many organizations to outsource their data in an encrypted form to improve processing times. The public Internet was not initially designed to handle massive quantities of data flowing through millions of networks. Thus, the rapid increase in broadcast users and growth in the amount of broadcasted information leads to a decrease in the speed of sending queries and receiving encrypted data from the cloud. In order to address this issue, Next Generation Internet (NGI) is being developed, capable of high speeds, while maintaining data privacy. This research proposes a novel search algorithm, entitled Multi-broadcast Searchable Keywords Encryption, which processes queries through a set of keywords. This set of keywords is sent from the users to the cloud server in an encrypted form, thus hiding all information about the user and the content of the queries from the cloud server. The proposed method uses a caching algorithm and provides an improvement of 40% in terms of runtime and trapdoor. In addition, the method minimizes computational costs, complexity, and maximizes throughput, in the cloud environment, whilst maintaining privacy and confidentiality of both the user and the cloud. The cloud returns encrypted query results to the user, where data is decrypted using the user's private keys.
AB - Cloud computing is a technology which has enabled many organizations to outsource their data in an encrypted form to improve processing times. The public Internet was not initially designed to handle massive quantities of data flowing through millions of networks. Thus, the rapid increase in broadcast users and growth in the amount of broadcasted information leads to a decrease in the speed of sending queries and receiving encrypted data from the cloud. In order to address this issue, Next Generation Internet (NGI) is being developed, capable of high speeds, while maintaining data privacy. This research proposes a novel search algorithm, entitled Multi-broadcast Searchable Keywords Encryption, which processes queries through a set of keywords. This set of keywords is sent from the users to the cloud server in an encrypted form, thus hiding all information about the user and the content of the queries from the cloud server. The proposed method uses a caching algorithm and provides an improvement of 40% in terms of runtime and trapdoor. In addition, the method minimizes computational costs, complexity, and maximizes throughput, in the cloud environment, whilst maintaining privacy and confidentiality of both the user and the cloud. The cloud returns encrypted query results to the user, where data is decrypted using the user's private keys.
KW - Coding and information theory
KW - Cryptography
KW - Data encryption
UR - http://www.scopus.com/inward/record.url?scp=85074760394&partnerID=8YFLogxK
U2 - 10.1016/j.adhoc.2019.102028
DO - 10.1016/j.adhoc.2019.102028
M3 - Article
AN - SCOPUS:85074760394
SN - 1570-8705
VL - 98
JO - Ad Hoc Networks
JF - Ad Hoc Networks
M1 - 102028
ER -