The exponential growth of machine learning applications in healthcare, finance, and cloud computing has raised significant data privacy concerns, as traditional encryption methods require access to decrypted data for processing, creating security vulnerabilities. Regulatory frameworks including the European Union's General Data Protection Regulation and the Health Insurance Portability and Accountability Act mandate privacy protection throughout the entire data management lifecycle. This research develops a privacy-preserving machine learning framework combining CKKS-based homomorphic encryption with Logistic Regression and AdaBoost classifiers using the UCI Adult Income dataset containing 4... More >