Privacy-Preserving Cyber Forensic Analysis Using Encrypted Feature Vectors for Secure Investigations

Authors- Madhudhara.V, Sivapriya. R, Sudharshini. C, D. Suganthi, J Mythili, Dr. N. Prabhu

Abstract-Securing sensitive data while allowing forensic analysis is becoming more important as digital data use rises. Machine learning-based cyber forensic investigations rely on feature vectors, which quantify digital data. However, these vectors typically include important data that must be securely stored. This work uses complex cryptographic methods including AES, RSA, and homomorphic encryption to encrypt feature vectors. Cyber forensics analyse encrypted routes to find abnormalities, track digital footprints, and uncover security breaches while protecting data. This innovation improves cybersecurity by allowing secure investigations without losing secrecy using encrypted forensic analysis. Results show that encrypted feature vectors may be handled privacy-preserving, retaining data integrity and investigative accuracy. In cyber forensics, this method affects malware detection, intrusion detection, and safe digital evidence management.

DOI: /10.61463/ijset.vol.12.issue5.810