Digital forensics plays a crucial role in cybercrime investigations. However, conventional forensic techniques often require access to unencrypted data, posing significant privacy risks. This project proposes a novel framework for Privacy- Preserving Forensic Analysis using Homomorphic Encryption, allowing investigators to perform operations on encrypted evidence without ever decrypting it. This ensures confidentiality and integrity of sensitive data during forensic analysis. By utilizing state-of-the-art cryptographic tools, the system preserves individual privacy while enabling admissible and verifiable forensic results. Privacy can be enhanced by using all the forensic computations performed using Cheon-Kim-Kim-Song on the encrypted data as the system never decrypts the evidence at any stage. As we have key generation, access control feature and end to end encrypted communication to ensure that data is confidential. This paper focuses on implementation of Cheon-Kim-Kim-Song Homomorphic Encryption scheme to perform the search operation, SQL Forensics Query, evidence matching and identify the anomalies on encrypted data by using python libraries.