A Novel Ide Based Privacy Preserving Method For Big Data Using Paritial Least Square Regression and ε-Differential Privacy Algorithms - Antony, Johny P; Thanamani, Antony Selvadoss (International Journal of Computer Sciences and Engineering, 2025)
Privacy preservation in big data is a need of the time because of the specialties of the data. Many researches have been made to tackle the issues of privacy in big data still some conflicts arises. Hence, an efficient method for the privacy preservation of data should be introduced. In this proposed work, a novel framework is designed for conserving the data in a secure manner. The personal and medical datasets are taken and are being merged which is under the control of hospital admin. This dataset is preprocessed to remove the noise following the normalization technique in order to convert the string into integers. Then, the efficient partial least square regression model is applied for the extraction of features such as sensitive and non-sensitive attributes. After the identification of this sensitive and non-sensitive attributes, ε-differential privacy preservation algorithm, the sensitive data are encrypted with the use of novel identity based encryption technique by generating the key. By the use of this code the user can decrypt the data which is anonymous format. The performance analysis is made on comparing the existing techniques which shows that the proposed methodology provides a better efficiency in terms of encryption cost, key generation cost, overall execution cost, security scheme, and computation complexity