Research on Privacy Preservation for Cloud Security focuses on developing methods to protect user data, identities, and operations from unauthorized access and inference while maintaining functionality, scalability, and performance in cloud environments. This area addresses challenges such as data confidentiality in multi-tenant systems, secure data outsourcing, compliance with privacy regulations, and potential misuse by untrusted cloud providers. Key research directions include encryption-based data protection (e.g., homomorphic encryption, searchable encryption), differential privacy for data analytics, and privacy-preserving access control and authentication mechanisms. Other emerging topics involve federated learning for decentralized privacy-preserving computation, blockchain-enabled privacy frameworks, and secure multi-party computation for collaborative cloud processing. Additionally, research on privacy-aware data sharing, context-aware privacy policies, and AI-driven privacy threat detection represents significant avenues for advancing trustworthy, secure, and user-centric cloud computing systems.