This project aims to develop privacy-preserving deepfake detection techniques that enable accurate and secure identification of synthetic audio and video content without exposing sensitive user data. Traditional detection methods often require access to raw audio or visual inputs, raising significant privacy concerns, especially in scenarios involving personal or biometric data. Leveraging techniques such as federated learning, differential privacy, and secure multi-party computation, this project seeks to design detection frameworks that maintain high performance while ensuring user data remains decentralized and protected. The outcome will contribute to trustworthy and ethically aligned AI systems that can be deployed in real-world environments, such as social media platforms and communication apps, where privacy and security are paramount.