Article: A blockchain-aided privacy preservation using lattice homomorphic encryption for digital forensic investigation Journal: International Journal of Information and Computer Security (IJICS) 2025 Vol.27 No.3 pp.333 - 363 Abstract: 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%. Inderscience Publishers - linking academia, business and industry through research

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

Authors: Suvarna Chaure; Vanita Mane

Addresses: Department of Computer Engineering, Ramrao Adik Institute of Technology, DY Patil Deemed to be University, Nerul, Maharashtra, 400705, India; Department of Computer Engineering, SIES Graduate School of Technology, Mumbai University, Nerul, Maharashtra, 400705, India ' Department of Computer Engineering, Ramrao Adik Institute of Technology, DY Patil Deemed to be University, Nerul, Maharashtra, 400705, India

Abstract: 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%.

Keywords: support vector machine; interplanetary file storage system; pelican optimisation; digital forensic investigation data; homomorphic encryption.

DOI: 10.1504/IJICS.2025.147754

International Journal of Information and Computer Security, 2025 Vol.27 No.3, pp.333 - 363

Received: 19 Mar 2024
Accepted: 16 Oct 2024
Published online: 30 Jul 2025 *

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

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