Privacy-Preserving and Scalable Digital Evidence Management: A Hyperledger Fabric Architecture with Growth Projections for Law Enforcement

Managing digital evidence presents challenges in scalability, security, and workflow separation. This paper presents a novel architecture using Hyperledger Fabric that separates law enforcement, forensic, and judicial workflows into different channels and integrates IPFS for scalable evidence storage. This design addresses three critical challenges: storage scalability, workflow separation, and privacy preservation. A deterministic time series model based on HMICFRS data (2020–2022) projects the demands of digital forensic storage through 2030. The analysis forecasts that growth to approximately 68,000 devices will require examination and translates to 8.5 PB of base storage. This escalates to 11.3 PB with encryption overhead (33%) and requires an additional 0.17 PB to preserve evidence metadata (2%). These projections inform the proposed distributed architecture. Performance evaluation with up to 10,000 transactions achieved throughput rates of 308–384 TPS with latencies below 3.23 milliseconds. Storage tests demonstrated encryption times of 0.003 seconds and upload speeds of 4.35 MB/s for evidence fragments up to 1GB. The system supports workflow management, ensures complete evidence traceability through privacy-preserving blockchain channels, and enables cross-jurisdictional compliance. It meets current digital evidence management standards with an architecture designed to scale to accommodate projected growth.