Data Privacy-Preservation: A Method of Machine Learning
DOI:
https://doi.org/10.18034/abcjar.v7i2.654Keywords:
Cloud, Cyber Security, Machine Learning, Privacy PreservationAbstract
The privacy-preservation field in cyber security tends to affiliate with the protection measure related to the use of data and its sharing via third parties for activities such as data analysis. The paper's main objective for this research article will be to use machine learning models that tend to aid as a privacy-preservation technique (PPT). The augmentation of machine learning as a technique for privacy preservation has been able to address the challenges facing the current field of cyber security concerning data protection and security. The paper summarizes the methods such as "federated learning" to address the current issue in the network security field relating to data protection. The rise of augmentation of machine learning in privacy preservation is due to the development of cloud-based applications that are usually prone to data protection issues. Thus, the result of machine learning was necessary to counteract data insecurity. However, the use of machine learning in privacy preservation has remained proficient; there still needs to be a literature gap between the theory and the application of machine learning.
Downloads
References
Achar, S. (2015). Requirement of Cloud Analytics and Distributed Cloud Computing: An Initial Overview. International Journal of Reciprocal Symmetry and Physical Sciences, 2, 12–18. Retrieved from https://upright.pub/index.php/ijrsps/article/view/70 DOI: https://doi.org/10.18034/ijrsps.v2.70
Achar, S. (2016). Software as a Service (SaaS) as Cloud Computing: Security and Risk vs. Technological Complexity. Engineering International, 4(2), 79-88. https://doi.org/10.18034/ei.v4i2.633 DOI: https://doi.org/10.18034/ei.v4i2.633
Achar, S. (2017). Asthma Patients’ Cloud-Based Health Tracking and Monitoring System in Designed Flashpoint. Malaysian Journal of Medical and Biological Research, 4(2), 159-166. https://doi.org/10.18034/mjmbr.v4i2.648 DOI: https://doi.org/10.18034/mjmbr.v4i2.648
Adusumalli, H. P. (2016). How Big Data is Driving Digital Transformation?. ABC Journal of Advanced Research, 5(2), 131-138. https://doi.org/10.18034/abcjar.v5i2.616
Borgia, E. (2014). The Internet of Things vision: Key features, applications and open issues. Comput. Commun., 54, 1–31. DOI: https://doi.org/10.1016/j.comcom.2014.09.008
Cai, Y., Dai, D., & Hua, S. (2016). Using machine learning algorithms to improve the prediction accuracy in disease identification: An empirical example. Athens: The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp). Retrieved from https://search.proquest.com/docview/1806429009?accountid=35493
Cassel, C. K. J. J. (2012). Retail clinics and drugstore medicine. 307(20), 2151-2152. DOI: https://doi.org/10.1001/jama.2012.3966
Chen, M., Hao, Y., Hwang, K., Wang, L., & Wang, L. J. I. A. (2017). Disease prediction by machine learning over big data from healthcare communities. 5, 8869-8879. DOI: https://doi.org/10.1109/ACCESS.2017.2694446
Dehury, C. K., Sahoo, P. K. (2016). Design and implementation of a novel service management framework for IoT devices in cloud. J. Syst. Softw., 119, 149–161. DOI: https://doi.org/10.1016/j.jss.2016.06.059
Fadziso, T., Adusumalli, H. P., & Pasupuleti, M. B. (2018). Cloud of Things and Interworking IoT Platform: Strategy and Execution Overviews. Asian Journal of Applied Science and Engineering, 7, 85–92. https://upright.pub/index.php/ajase/article/view/63
Iyawa, G. E., Herselman, M., & Botha, A. (2017). A scoping review of digital health innovation ecosystems in developed and developing countries. Piscataway: The Institute of Electrical and Electronics Engineers, Inc. (IEEE). Retrieved from https://search.proquest.com/docview/1962316664?accountid=35493 DOI: https://doi.org/10.23919/ISTAFRICA.2017.8102325
Michalski, R.S., Carbonell, J.G., Mitchell, T.M. (1983). Machine Learning: An Artificial Intelligence Approach. Springer, https://www.springer.com/gp/book/9783662124079 DOI: https://doi.org/10.1007/978-3-662-12405-5
Pasupuleti, M. B. (2015). Data Science: The Sexiest Job in this Century. International Journal of Reciprocal Symmetry and Physical Sciences, 2, 8–11. https://upright.pub/index.php/ijrsps/article/view/56
Ray, P. P. (2016). A survey of IoT cloud platforms. Future Comput. Inform. J., 1, 35–46. DOI: https://doi.org/10.1016/j.fcij.2017.02.001
Truong, H. L., Dustdar, S. (2015). Principles for engineering IoT cloud systems. IEEE Cloud Comput., 2, 68–76. DOI: https://doi.org/10.1109/MCC.2015.23
Xia, F., Yang, L.T., Wang, L., Vinel, A. (2012). Internet of things. Int. J. Commun. Syst., 25, 1101–1102. DOI: https://doi.org/10.1002/dac.2417
--0--
Downloads
Published
Issue
Section
License
Copyright (c) 2018 Sandesh Achar
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.