This article presents a comprehensive framework for a Privacy-Preserving Movie Recommendation System \(PPMRS\) . The exponential growth of recommendation systems necessitates the protection of user privacy. This study outlines the background, objectives, and scope, emphasizing privacy-preserving techniques such as Local Differential Privacy \(LDP\) and Advanced Encryption Standard \(AES\) . It thoroughly examines various recommendation methodologies, including collaborative filtering, content-based filtering, and hybrid filtering, highlighting the associated privacy issues. The proposed PPMRS architecture integrates data collection, storage, preprocessing, and privacy preservation mechanisms. Detailed implementation of LDP and AES is provided, including their mechanisms and parameters. The systems effectiveness is evaluated through performance metrics and analysis, demonstrating its ability to maintain high-quality recommendations while ensuring robust privacy. The findings, limitations, and potential directions for future research are discussed.