Document Type : Research Article
Author
Department of Information System, College of Business, University of Jeddah, Jeddah, Saudi Arabia
Abstract
Today, in the area of telecommunication, social media, the internet of things (IoT), and the virtual world, enormous amounts of data are being generated which are extracted to discover knowledge. Knowledge discovery from data in the cloud-computing environment entails the extraction of the new and necessary information from the large and complex datasets. This study is qualitative and exploratory in nature. To review based on the recent literature, the articles published in the last five years (2014-2018) were searched. Different databases were searched using the keywords: ‘Knowledge management’ or ‘Knowledge discover*’ and ‘Cloud computing.'. The literature review section is divided into three sub-section based on the findings. The first two sub-sections present the data security and data privacy concerns under two main techniques (Big data analytics and machine learning) used in knowledge discovery, and the last sub-section presents various protocols proposed to address the related security and privacy concerns. This review consolidates the related data security and privacy challenges under two techniques used for knowledge discovery in a cloud environment. Also, the review consolidates the proposals proposed by different experts to address the data security and privacy concerned
Keywords
1] Derya Birant and Pelin Yıldırım. A framework for data mining and knowledge discovery in cloud computing. In Data Science and Big Data Computing, pages 245–267. Springer, 2016.
[2] Edmon Begoli and James Horey. Design principles for effective knowledge discovery from big data. In 2012 Joint Working IEEE/IFIP Conference on Software Architecture and European Conference on Software Architecture, pages 215– 218. IEEE, 2012.
[3] Kun Gao, Qin Wang, and Lifeng Xi. Reduct algorithm based execution times prediction in knowledge discovery cloud computing environment. Int. Arab J. Inf. Technol., 11(3):268–275, 2014.
[4] Domenico Talia. Making knowledge discovery services scalable on clouds for big data mining. In 2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM), pages 1–4. IEEE, 2015.
[5] Nabil Sultan. Knowledge management in the age of cloud computing and web 2.0: Experiencing the power of disruptive innovations. International journal of information management, 33(1):160– 165, 2013.
[6] Nir Kshetri. Big data impact on privacy, security and consumer welfare. Telecommunications Policy, 38(11):1134–1145, 2014.
[7] Christos Stergiou, Kostas E Psannis, Byung-Gyu Kim, and Brij Gupta. Secure integration of iot and cloud computing. Future Generation Computer Systems, 78:964–975, 2018.
[8] Maricela-Georgiana Avram. Advantages and challenges of adopting cloud computing from an enterprise perspective. Procedia Technology, 12: 529–534, 2014.
[9] Charith Perera, Rajiv Ranjan, Lizhe Wang, Samee U Khan, and Albert Y Zomaya. Big data privacy in the internet of things era. IT Professional, 17(3):32–39, 2015.
[10] Munsif Yousef Sokiyna, Musbah J Aqel, and Omar A Naqshbandi. Cloud computing technology algorithms capabilities in managing and processing big data in business organizations: Mapreduce, hadoop, parallel programming. Journal of Information Technology Management, 12(3): 100–113, 2020.
[11] Mohamed Almorsy, John Grundy, and Ingo M¨uller. An analysis of the cloud computing security problem. arXiv preprint arXiv:1609.01107, 2016.
[12] Nicolas Papernot, Patrick McDaniel, Arunesh Sinha, and Michael P Wellman. Sok: Security and privacy in machine learning. In 2018 IEEE European Symposium on Security and Privacy (EuroS&P), pages 399–414. IEEE, 2018.
[13] Andreas Holzinger, Matthias Dehmer, and Igor Jurisica. Knowledge discovery and interactive data mining in bioinformatics-state-of-the-art, future challenges and research directions. BMC bioinformatics, 15(6):1–9, 2014. [14] Ping Li, Jin Li, Zhengan Huang, Tong Li, ChongZhi Gao, Siu-Ming Yiu, and Kai Chen. Multi-key privacy-preserving deep learning in cloud computing. Future Generation Computer Systems, 74:76–85, 2017.
[15] Ibrahim Arpaci. Antecedents and consequences of cloud computing adoption in education to achieve knowledge management. Computers in Human Behavior, 70:382–390, 2017.
[16] Chaowei Yang, Qunying Huang, Zhenlong Li, Kai Liu, and Fei Hu. Big data and cloud computing: innovation opportunities and challenges. International Journal of Digital Earth, 10(1):13–53, 2017.
[17] Jun Zhou, Zhenfu Cao, Xiaolei Dong, and Athanasios V Vasilakos. Security and privacy for cloudbased iot: Challenges. IEEE Communications Magazine, 55(1):26–33, 2017.
[18] M Reza Rahimi, Jian Ren, Chi Harold Liu, Athanasios V Vasilakos, and Nalini Venkatasubramanian. Mobile cloud computing: A survey, state of art and future directions. Mobile Networks and Applications, 19(2):133–143, 2014. [19] Ning Cao, Cong Wang, Ming Li, Kui Ren, and Wenjing Lou. Privacy-preserving multi-keyword ranked search over encrypted cloud data. IEEE Transactions on parallel and distributed systems, 25(1):222–233, 2013.
[20] T Ramathulasi, C Samba Shiva Reddy, and K Ashok Kumar. Data encryption strategy with privacy-preserving for big data in mobile cloud using h2hadoop. 2018.
[21] Zhangjie Fu, Jiangang Shu, Jin Wang, Yuling Liu, and Sungyoung Lee. Privacy-preserving smart similarity search based on simhash over encrypted data in cloud computing. Journal of Internet Technology, 16(3):453–460, 2015. [22] Munsif Sokiyna and Musbah Aqel. The role of e-business applications software in driving operational excellence: Impact of departments collaboration using sustainable software. Sustainable Computing: Informatics and Systems, 28:100445, 2020.
[23] Joseph K Liu, Man Ho Au, Xinyi Huang, Rongxing Lu, and Jin Li. Fine-grained two-factor access control for web-based cloud computing services. IEEE Transactions on Information Forensics and Security, 11(3):484–497, 2015. [24] Xun Yi, Fang-Yu Rao, Elisa Bertino, and Athman Bouguettaya. Privacy-preserving association rule mining in cloud computing. In Proceedings of the 10th ACM symposium on information, computer and communications security, pages 439–450, 2015.
[25] Yong Yu, Man Ho Au, Giuseppe Ateniese, Xinyi Huang, Willy Susilo, Yuanshun Dai, and Geyong Min. Identity-based remote data integrity checking with perfect data privacy preserving for cloud storage. IEEE Transactions on Information Forensics and Security, 12(4):767–778, 2016.
[26] Yunchuan Sun, Junsheng Zhang, Yongping Xiong, and Guangyu Zhu. Data security and privacy in cloud computing. International Journal of Distributed Sensor Networks, 10(7):190903, 2014.
[27] Alessio Botta, Walter De Donato, Valerio Persico, and Antonio Pescap´e. Integration of cloud computing and internet of things: a survey. Future generation computer systems, 56:684–700, 2016.
[28] Jun Zhou, Zhenfu Cao, Xiaolei Dong, and Xiaodong Lin. Tr-mabe: White-box traceable and revocable multi-authority attribute-based encryption and its applications to multi-level privacypreserving e-healthcare cloud computing systems. In 2015 IEEE Conference on Computer Communications (INFOCOM), pages 2398–2406. IEEE, 2015.
[29] Lifei Wei, Haojin Zhu, Zhenfu Cao, Xiaolei Dong, Weiwei Jia, Yunlu Chen, and Athanasios V Vasilakos. Security and privacy for storage and computation in cloud computing. Information sciences, 258:371–386, 2014.
[30] Zhihua Xia, Neal N Xiong, Athanasios V Vasilakos, and Xingming Sun. Epcbir: An efficient and privacy-preserving content-based image retrieval scheme in cloud computing. Information Sciences, 387:195–204, 2017. [31] Qingchen Zhang, Laurence T Yang, Zhikui Chen, and Peng Li. Pphopcm: Privacy-preserving highorder possibilistic c-means algorithm for big data clustering with cloud computing. IEEE Transactions on Big Data, 2017. [32] Jiawei Yuan and Shucheng Yu. Privacy preserving back-propagation neural network learning made practical with cloud computing. IEEE Transactions on Parallel and Distributed Systems, 25(1): 212–221, 2013.
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