Performance Analysis of the Platfora Method for Privacy Preservation in Large Healthcare Datasets: An Empirical Study
DOI:
https://doi.org/10.29070/vts5p257Keywords:
databases, Preserving Data, datasets, digitalization, Health careAbstract
Multimedia files and private medical information are among the many kinds of data stored in these systems. Data mining offers a promising method for many businesses and organizations to sift through massive data stores in search of useful insights. There are legitimate worries about privacy invasion and data abuse associated with extracting sensitive information from these databases. Personal information such as names, ages, residences, and phone numbers is included in healthcare data, along with sensitive details such the names and characteristics of diseases. Improper handling of this information might lead to its misuse for personal advantage. Hence, it is crucial to conceal data from enemies before sharing it with outside parties. One new approach that promises to solve these privacy problems is privacy-preserving data mining, or PPDM. Protecting private data from unauthorized third-party suppliers is the primary objective of PPDM. Various methods for protecting personal information have been proposed in this study. To conduct the experiment, various patient pilot datasets were considered. We apply a JAYA-based genetic algorithm to conceal information in our contribution.
References
Welten, Sascha & Mou, Yongli & Neumann, Laurenz & Jaberansary, Mehrshad & Ucer, Yeliz & Kirsten, Toralf & Decker, Stefan & Beyan, Oya. (2022). A Privacy-Preserving Distributed Analytics Platform for Health Care Data. Methods of Information in Medicine. 61. 10.1055/s-0041-1740564.
Rajan. V S, Dr. (2015). Platfora Method for High Data Delivery in Large Datasets. Indian Journal of Science and Technology. 8. 10.17485/ijst/2015/v8i33/7651.
Hamza, Rafik & Dao, Minh. (2022). Research on privacy-preserving techniques in the era of the 5G applications. Virtual Reality & Intelligent Hardware. 4. 210-222. 10.1016/j.vrih.2022.01.007.
Safaei Yaraziz, Mahdi & Jalili, Ahmad & Gheisari, Mehdi & Liu, Yang. (2022). Recent trends towards privacy‐preservation in Internet of Things, its challenges and future directions. IET Circuits, Devices & Systems. 17. n/a-n/a. 10.1049/cds2.12138.
Zhang, Denghui & Shafiq, Muhammad & Wang, Liguo & Srivastava, Gautam & Yin, Shoulin. (2023). Privacy‐preserving remote sensing images recognition based on limited visual cryptography. CAAI Transactions on Intelligence Technology. 8. n/a-n/a. 10.1049/cit2.12164.
Ambulkar, B., & Borkar, V. (2012, April). Data mining in cloud computing. In MPGI National Multi Conference (Vol. 2012). Academic Press.
Anjum, A., Ahmed, T., Khan, A., Ahmad, N., Ahmad, M., Asif, M., Reddy, A. G., Saba, T., & Farooq, N. (2018). Privacy preserving data by conceptualizing smart cities using MIDR-Angelization. Sustainable Cities and Society, 40, 326–334. doi:10.1016/j.scs.2018.04.014
Arthur, D., & Vassilvitskii, S. (2006c, October). Worst-case and smoothed analysis of the ICP algorithm, with an application to the k-means method. In 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS’06) (pp. 153-164). IEEE. doi:10.1109/FOCS.2006.79
Chamikara, M. A. P., Bertók, P., Liu, D., Camtepe, S., & Khalil, I. (2018). Efficient data perturbation for privacy preserving and accurate data stream mining. Pervasive and Mobile Computing, 48, 1–19. doi:10.1016/j. pmcj.2018.05.003
Kikuchi, H., Hamanaga, C., Yasunaga, H., Matsui, H., Hashimoto, H., & Fan, C. I. (2018). Privacy-preserving multiple linear regression of vertically partitioned real medical datasets. Journal of Information Processing, 26(0), 638–647. doi:10.2197/ipsjjip.26.638
Komishani, E. G., Abadi, M., & Deldar, F. (2016). PPTD: Preserving personalized privacy in trajectory data publishing by sensitive attribute generalization and trajectory local suppression. Knowledge-Based Systems, 94, 43–59. doi: 10.1016/j.knosys.2015.11.007
Li, L., Lu, R., Choo, K. K. R., Datta, A., & Shao, J. (2016). Privacy-preserving-outsourced association rule mining on vertically partitioned databases. IEEE Transactions on Information Forensics and Security, 11(8), 1847–1861. doi:10.1109/TIFS.2016.2561241
Li, Y., Jiang, Z. L., Yao, L., Wang, X., Yiu, S. M., & Huang, Z. (2019). Outsourced privacy-preserving C4. 5 decision tree algorithms over horizontally and vertically partitioned dataset among multiple parties. Cluster Computing, 22(1), 1581–1593. doi:10.1007/s10586-017-1019-9
Lin, J. C. W., Wu, T. Y., Fournier-Viger, P., Lin, G., Zhan, J., & Voznak, M. (2016). Fast algorithms for hiding sensitive high-utility itemsets in privacy-preserving utility mining. Engineering Applications of Artificial Intelligence, 55, 269–284. doi: 10.1016/j.engappai.2016.07.003
Upadhyay, S., Sharma, C., Sharma, P., Bharadwaj, P., & Seeja, K. R. (2018). Privacy preserving data mining with 3-D rotation transformation. Journal of King Saud University-Computer and Information Sciences, 30(4), 524–530. doi: 10.1016/j.jksuci.2016.11.009
This work is licensed under a 