Secure Cross-Chain Provenance for Digital Forensics Collaboration
<|reference_start|>Secure Cross-Chain Provenance for Digital Forensics Collaboration: In digital forensics and various sectors like medicine and supply chain, blockchains play a crucial role in providing a secure and tamper-resistant system that meticulously records every detail, ensuring accountability. However, collaboration among different agencies, each with its own blockchains, creates challenges due to diverse protocols and a lack of interoperability, hindering seamless information sharing. Cross-chain technology has been introduced to address these challenges. Current research about blockchains in digital forensics, tends to focus on individual agencies, lacking a comprehensive approach to collaboration and the essential aspect of cross-chain functionality. This emphasizes the necessity for a framework capable of effectively addressing challenges in securely sharing case information, implementing access controls, and capturing provenance data across interconnected blockchains. Our solution, ForensiCross, is the first cross-chain solution specifically designed for digital forensics and provenance. It includes BridgeChain and features a unique communication protocol for cross-chain and multi-chain solutions. ForensiCross offers meticulous provenance capture and extraction methods, mathematical analysis to ensure reliability, scalability considerations for a distributed intermediary in collaborative blockchain contexts, and robust security measures against potential vulnerabilities and attacks. Analysis and evaluation results indicate that ForensiCross is secure and, despite a slight increase in communication time, outperforms in node count efficiency and has secure provenance extraction. As an all-encompassing solution, ForensiCross aims to simplify collaborative investigations by ensuring data integrity and traceability.<|reference_end|>
@article{akbarfam2024secure,
    title={Secure Cross-Chain Provenance for Digital Forensics Collaboration},
    author={Asma Jodeiri Akbarfam, Gokila Dorai, Hoda Maleki},
    journal={arXiv preprint arXiv:2406.11729},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.11729},
    primaryClass={cs.CR cs.SI}
}