Privacy-preserving cloud forensics focuses on developing frameworks and methodologies that enable the acquisition, analysis, and preservation of digital evidence in cloud environments while protecting sensitive user data. Research in this domain explores techniques that balance forensic soundness with data privacy, ensuring legal compliance and confidentiality in multi-tenant and cross-border cloud infrastructures. Key topics include secure data collection using encryption and anonymization, privacy-aware evidence correlation across distributed cloud services, and access-controlled forensic workflows. Other significant areas involve the integration of homomorphic encryption and secure multi-party computation for analyzing encrypted data, blockchain-based mechanisms for tamper-proof and auditable evidence management, and AI-driven anomaly detection without compromising user privacy. Emerging directions also explore federated forensic models for decentralized investigation, privacy-preserving logging and provenance tracking, and compliance frameworks for regulations such as GDPR and HIPAA. Developing scalable, legally admissible, and privacy-conscious cloud forensic models remains a critical research challenge in modern digital investigations.