International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Privacy-Preserving Distributed Training Architecture for Cyber Forensics using Blockchain and Homomorphic Encryption |
|---|---|
| ABSTRACT | The rapid growth of cybercrime, ransomware attacks, digital fraud, and large-scale cyber threats has significantly increased the need for secure and collaborative cyber forensic investigations. Traditional machine learning approaches often require organizations to share or centralize sensitive forensic datasets, creating challenges related to privacy, confidentiality, data ownership, and security. To address these limitations, this project proposes a Privacy-Preserving Distributed Training Architecture for Cyber Forensics using Blockchain and Homomorphic Encryption. The proposed framework integrates Federated Learning, Distributed Learning, CKKS-based Homomorphic Encryption, Blockchain Technology, and a Secure Model Exchange Space to enable multiple agencies to collaboratively train machine learning models without exposing their raw forensic data. Federated Learning allows organizations to train models locally and securely aggregate encrypted model updates, while Distributed Learning enables encrypted dataset partitions to be processed collaboratively by helper nodes without revealing the original data. CKKS Homomorphic Encryption protects sensitive information during computation, and blockchain technology provides decentralized trust through secure node authentication, transparent validation, immutable audit trails, and trusted model exchange among participating agencies. The framework is implemented using Python, Flask, Scikit-learn, TenSEAL, Ganache, Solidity, and Web3.py, providing a web-based platform for collaborative project management, encrypted training, blockchain monitoring, secure model sharing, performance evaluation, and cyber forensic prediction. Experimental results demonstrate that the proposed architecture successfully supports secure collaborative learning, encrypted computation, blockchain-based validation, and trusted model sharing while maintaining effective prediction performance. By integrating distributed learning, federated learning, homomorphic encryption, and blockchain into a unified framework, the proposed system provides a scalable, secure, and privacy-preserving solution for next-generation cyber forensic intelligence, enabling organizations to collaboratively strengthen cybersecurity without compromising the privacy, confidentiality, or ownership of sensitive forensic data. |
| AUTHOR | DR. R. SRIDEVI, P. SANJAY KUMAR Professor, Department of Computer Science Engineering, Jawaharlal Nehru Technological University, Hyderabad, Telangana, India Post-Graduate Student, Department of Computer Science Engineering, Jawaharlal Nehru Technological University, Hyderabad, Telangana, India |
| VOLUME | 186 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1407058 |
| pdf/58_Privacy-Preserving Distributed Training Architecture for Cyber Forensics using Blockchain and Homomorphic Encryption.pdf | |
| KEYWORDS | |
| References | 1. R. Yang et al., "Blockchain-Based Federated Learning With Enhanced Privacy and Security Using Homomorphic Encryption and Reputation," IEEE Internet of Things Journal, vol. 11, no. 12, pp. 21674–21688, June 2024, DOI: 10.1109/JIOT.2024.3379395. 2. P. Pushpa et al., "Homomorphic Encryption for Enhanced Cybersecurity Privacy-Preserving Data Processing in Cloud-based Systems," in Proc. 2025 6th International Conference for Emerging Technology (INCET), Belgaum, India, May 23–25, 2025, DOI: 10.1109/INCET64471.2025.11140360. 3. Prokash Gogoi and J. Arul Valan, "Application of Homomorphic Encryption in Machine Learning Based Chronic Kidney Disease Prediction," in Proc. 2024 International Conference (WCONF), July 12–14, 2024, DOI: 10.1109/WCONF61366.2024.10692276. 4. W. Fan, S. Li, J. Liu, Y. Su, F. Wu, and Y. Liu, “Joint task offloading and resource allocation for accuracy-aware machine-learning-based IIoT applications,” IEEE Internet Things J., vol. 10, no. 4, pp. 3305–3321, Feb. 2023. 5. J. Zhang, C. Luo, M. Carpenter, and G. Min, “Federated learning for distributed IIoT intrusion detection using transfer approaches,” IEEE Trans. Ind. Informat., vol. 19, no. 7, pp. 8159–8169, Jul. 2023. 6. Y. Jiang, Y. Zhong, and X. Ge, “IIoT data sharing based on blockchain: A multileader multifollower Stackelberg game approach,” IEEE Internet Things J., vol. 9, no. 6, pp. 4396–4410, Mar. 2022. 7. Q. Li et al., “A survey on federated learning systems: Vision, hype and reality for data privacy and protection,” IEEE Trans. Knowl. Data Eng., vol. 35, no. 4, pp. 3347–3366, Apr. 2023. 8. M. M. Salim, I. Kim, U. Doniyor, C. Lee, and J. H. Park, “Homomorphic Encryption Based Privacy-Preservation for IOMT,” Applied Sciences, vol. 11, no. 18, p. 8757, Sep. 2021, doi: 10.3390/app11188757. 9. S. Li, S. Zhao, G. Min, L. Qi, and G. Liu, “Lightweight Privacy Preserving scheme using homomorphic encryption in industrial internet of things,” IEEE Internet of Things Journal, vol. 9, no. 16, pp. 14542–14550, Aug. 2022, doi: 10.1109/JIOT.2021.3066427. |
