Analysing Privacy-Preserving Techniques in Machine Learning for Data Utility - Charuhasini, N; Drakshayani, P; Aparna, P. Dhana Sri; Pravalika, P; Praneeth, Ch (International Journal of Computer Sciences and Engineering, 2025)
Data privacy is a critical challenge in publicly shared datasets. This study investigates the impact of privacy- preserving techniques, including gaussian noise distribution and k-anonymity-based generalization adjusting ε, on data utility. Using a dataset related to stress prediction, we apply these techniques to safeguard sensitive attributes while assessing their impact on machine learning models. Logistic Regression, Random Forest, and k-Nearest Neighbours (KNN) are used to evaluate utility preservation. Our results highlight the trade-off between privacy and predictive performance, demonstrating that k-anonymity generalization maintains better model accuracy compared to noise addition. These findings contribute to privacy- aware machine learning, applicable to domains handling sensitive demographic and financial data