Abstract
Cloud computing is the need of the twenty-first century with an exponential increase in the volume of data. Compared to any other technologies, the cloud has seen fastest adoption in the industry. The popularity of cloud is closely linked to the benefits it offers which ranges from a group of stakeholders to huge number of entrepreneurs. This enables some prominent features such as elasticity, scalability, high availability, and accessibility. So, the increase in popularity of the cloud is linked to the influx of data that involves big data with some specialized techniques and tools. Many data analysis applications use clustering techniques incorporated with machine learning to derive useful information by grouping similar data, especially in healthcare and medical department for predicting symptoms of diseases. However, the security of healthcare data with a machine learning model for classifying patient’s information and genetic data is a major concern. So, to solve such problems, this paper proposes a Cloud-Machine Learning (CloudML) Model for encrypted heart disease datasets by employing a privacy preservation scheme in it. This model is designed in such a way that it does not vary in accuracy while clustering the datasets. The performance analysis of the model shows that the proposed approach yields significant results in terms of Communication Overhead, Storage Overhead, Runtime, Scalability, and Encryption Cost.
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References
Bunn, P., Ostrovsky, R.: Secure two party k-means clustering. In: Proceedings of the 14th ACM Conference on Computer and Communications Security, pp. 486–497. ACM (2007)
Cui, X., Zhu, P., Yang, X., Li, K., Ji, C.: Optimized big data k-means clustering using map reduce. J. Super Comput. 70(3), 1249–1259 (2014)
Doganay, M.C., Pedersen, T.B., Saygin, Y., Savas, E., Levi, A.: Distributed privacy preserving k-means clustering with additive secret sharing. In: Proceedings of the 2008 International Workshop on Privacy and Anonymity in Information Society, pp. 3–11. ACM (2008)
Fahim, A.M., Salem, A.M., Af Torkey, F., Ramadan, M.A.: An efficient enhanced k-means clustering algorithm. J. Zhejiang Univ. Sci. 7(10), 1626–1633 (2006)
Jagannathan, G., Wright, R.N.: Privacy preserving distributed k-means clustering over arbitrarily partitioned data. In: Proceedings of the Eleventh ACMSIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 593–599 (2005)
Sakuma, J., Kobayashi, S.: Large scale k-means clustering with user centric privacy -preservation. Knowl. Inf. Syst. 25(2), 253–279 (2010)
Sharma, I., Tiwari, R., Rana, H.S., Anand, A.: Analysis of mahout big data clustering algorithms. In: Singh, R., Choudhury, S., Gehlot, A. (eds.) Intelligent Communication, Control and Devices. Advances in Intelligent Systems and Computing, vol. 624. Springer, Singapore (2018)
Smys, S., Raj, J.S.: Internet of things and big data analytics for health care with cloud computing. J. Inf. Technol. Digit. World 01(01), 9–18 (2019)
Suma, V.: A novel information retrieval system for distributed cloud using hybrid deep fuzzy hashing algorithm. J. Inf. Technol. Digit. World 02(03), 151–160 (2020)
Vaidya, J., Clifton, C.: Privacy preserving k-means clustering over vertically partitioned data. In: Proceedings of the ninth ACMSIGKDD international conference on Knowledge discovery and data mining, pp. 206–215. ACM (2003)
Vaidya, J., Kantarcıoglu, M., Clifton, C.: Privacy preserving naive bayes classification. VLDB J. 17(4), 879–898 (2008)
Xu, X., Jager, J., Kriegel, H.P.: A fast parallel clustering algorithm for large spatial databases. In: High Performance Data Mining, pp. 263–290. Springer (1999)
Yuan, J., Tian, Y.: Practical privacy preserving map reduce based k-means clustering over large scale dataset. IEEE Trans. Cloud Comput. (2017)
Zhang, Q., Yang, L.T., Chen, Z., Li, P.: Pphopcm: Privacy preserving high order possibilistic c-means algorithm for big data clustering with cloud computing. IEEE Trans. BigData (2017)
Zhao, W., Ma, H., He, Q.: Parallel k-means clustering based on map reduce. In: IEEE International Conference on Cloud Computing, pp. 674–679. Springer (2009)
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Savitha, S., Ravichandran, S.K. (2022). CloudML: Privacy-Assured Healthcare Machine Learning Model for Cloud Network. In: Raj, J.S., Palanisamy, R., Perikos, I., Shi, Y. (eds) Intelligent Sustainable Systems. Lecture Notes in Networks and Systems, vol 213. Springer, Singapore. https://doi.org/10.1007/978-981-16-2422-3_5
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DOI: https://doi.org/10.1007/978-981-16-2422-3_5
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