AI-Powered Privacy Preservation: A Novel Framework for Adaptive Data Protection
R. Senthil Kumar, J. Lokeshwari, Selvanayaki Kolandapalayam Shanmugam, V. Thamarai Selvi · 2024
The enlargement of Artificial Intelligence (AI)-powered privacy preservation techniques leads to new research avenues and innovations. Moreover, in today's data driven world the privacy and data protection are increased. In order to balance both data utility and individual privacy rights. This research paper provides a unique AI-powered privacy preservation framework by using machine learning algorithm that adapts to diverse data scenarios. The proposed framework uses Adaptive Privacy-Preserving Ensemble Learning (APPEL) machine learning algorithms to ensure the optimal data protection while maintaining both data utility and also privacy settings dynamically. Additionally, this paper evaluates the efficacy of data driven applications like Healthcare, social media and Online Platforms, Internet of Things (IoT) and smart devices. This study collected and pre-processed the data from data driven applications to determine trends, insights, and highlighting key challenges and opportunities for improvement. This framework's efficacy in real-world applications demonstrate the experimental results and provides an accurate and promising solution for privacy-preserving data analysis. This research contributes to the development of privacy-preserving AI solutions to safeguard sensitive data in data driven environment.