Privacy-Preserving AI: Unlocking Data Potential through Differential Privacy

Abstract

As data-driven technologies continue to evolve, ensuring the privacy of individuals has become a fundamental challenge. This paper explores the transformative role of differential privacy in enabling privacy-preserving artificial intelligence. By incorporating controlled randomness into data processing, differential privacy obscures the influence of individual records while preserving the overall statistical integrity of datasets. This mechanism enables organizations to derive valuable insights without exposing sensitive information. The proposed approach supports compliance with global data protection standards and fosters trust in AI systems. Ultimately, privacy-preserving AI represents a paradigm shift where innovation and privacy coexist, enabling secure and ethical utilization of data in modern applications.