ZAKON: A decentralized framework for digital forensic admissibility and justification
2025 (English)In: Information Processing & Management, ISSN 0306-4573, E-ISSN 1873-5371, Vol. 62, no 6, article id 104226Article in journal (Refereed) Published
Abstract [en]
Digital forensics has become important in legal cases. It demands accuracy for fair outcomes. Despite advances in evidence handling and analysis, challenges persist in maintaining legal integrity, authenticity, and courtroom admissibility. Existing decentralized frameworks fail to ensure prosecutable evidence in the courtroom. In this paper, we introduce a general decentralized digital evidence framework to address the courtroom admissibility problem mentioned above. Our proposed decentraliZed frAmeworK for fOrensic justificatioN (ZAKON) ensures the ethical, legal, and privacy-based obligations through smart contracts and includes privacy-ensured query resolution. The main contribution of our ZAKON is to use a multidimensional checking for the admissibility of the evidence transactions. Another contribution of our ZAKON is the post-trial query resolution, which is important in crime investigation or making a referential understanding of the investigation processes in multi-case environments. We deploy our ZAKON framework on Hyperledger Fabric and measure the performance based on the Caliper benchmark metrics. ZAKON achieves notable performance improvements. It delivers an average throughput of 8320 TPS, about 70% higher than existing systems, and reduces average latency to 1.85 s, a 29.28% improvement. It maintains a 100% transaction success rate. Resource utilization remains moderate, with peak CPU usage at 4.5% and memory usage at 32.76 MB. ZAKON also ensures linear computational and communication complexity, enabling scalability for real-time forensic applications.
Place, publisher, year, edition, pages
Elsevier, 2025. Vol. 62, no 6, article id 104226
Keywords [en]
Digital, Forensic, Blockchain, Provenance, Law, Legal, Decentralized
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-122480DOI: 10.1016/j.ipm.2025.104226ISI: 001517307000001Scopus ID: 2-s2.0-105007615744OAI: oai:DiVA.org:oru-122480DiVA, id: diva2:1985475
2025-07-242025-07-242026-01-23Bibliographically approved