Privacy-Preserving Artificial Intelligence: Principles, Methods, Applications, and Challenges

Abstract

Artificial intelligence systems increasingly rely on large volumes of sensitive data to support decision making in domains such as healthcare, finance, education, and public administration. While these systems offer substantial benefits, their growing dependence on personal information has intensified concerns about privacy, data misuse, and loss of public trust. This study examines privacy-preserving artificial intelligence as a design approach that enables meaningful data analysis while limiting exposure of sensitive information. The paper analyses key privacy-preserving techniques, including federated learning, differential privacy, homomorphic encryption, and secure multi party computation, and evaluates their relevance across critical application domains. It also identifies practical challenges related to computational cost, data heterogeneity, regulatory compliance, and explainability. The study shows that privacy-preserving methods can support responsible and trustworthy artificial intelligence when privacy, utility, and governance considerations are addressed together.