Development and Evaluation of Privacy-Preserving Data Mining Algorithms for Cloud-Based Healthcare Analytics

Abstract

This manuscript discusses the methods of designing and implementing privacy-aware data mining algorithms relevant to the field of healthcare. As more and more healthcare institutions employ the cloud for the storage and analysis of health records, patient data protection, especially patient’s privacy, becomes a daunting problem. The study comprehends how complex technologies such as homomorphic encryption and differential privacy can be put into use in processes, which include manipulating patient data without revealing any identifiable information. This privacy-preservative technique allows healthcare practitioners and researchers to analyse a significant amount of medical information without ever breaching stringent regulations like HIPAA and GDPR. This paper focuses on the practical implementation of these algorithms to secure patient’s data during cloud computing analytics to the extent that even healthcare institutions can utilize big data without breaching confidentiality. Practical aspects of the implementation of the considered techniques into practice allow us to evaluate the efficiency of healthcare IT systems in conditions where both confidentiality, accuracy, and processing speed are important. In this respect, this paper is a step further in securing patient information within the healthcare delivers space and encouraging the beneficial and ethical practices of patient data in analytics and decision making.