A blockchain-aided privacy preservation using lattice homomorphic encryption for digital forensic investigation

This paper proposes a novel secrecy-preserving optimised machine learning-based digital forensic model (SOMLDFM) designed to address the computational complexities of existing forensic models. The model utilises a pelican optimisation-based hybrid support vector machine-extreme learning machine (SVM-ELM) for feature extraction and classification. This hybrid approach classifies files into forensically related files (FRFs) and forensically unrelated files (FNRFs) while effectively removing noise and irrelevant data. The Pelican optimisation technique reduces potential losses in the hybrid SVM-ELM, resulting in enhanced overall performance. To protect confidential information, the model employs lattice-based homomorphic encryption (LHE), which offers superior security compared to elliptic curve and Diffie-Hellman methods. The discovered files are prioritised based on a calculated relevance score, arranged from highest to lowest by the investigator. The proposed model demonstrates high performance, achieving an accuracy of 98.69%, an F1 score of 97.79%, a recall score of 97.15%, and a precision score of 98.44%.