English

Secure Cross-Chain Provenance for Digital Forensics Collaboration

Cryptography and Security 2024-10-23 v1 Social and Information Networks

Abstract

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.

Keywords

Cite

@article{arxiv.2406.11729,
  title  = {Secure Cross-Chain Provenance for Digital Forensics Collaboration},
  author = {Asma Jodeiri Akbarfam and Gokila Dorai and Hoda Maleki},
  journal= {arXiv preprint arXiv:2406.11729},
  year   = {2024}
}

Comments

This work has been submitted to the IEEE for possible publication

R2 v1 2026-06-28T17:08:56.471Z