Design and Implementation of a Digital Evidence Management Model Based on Hyperledger Fabric
- Authors
- Jeong, Junho; Kim, Donghyo; Lee, Byungdo; Son, Yunsik
- Issue Date
- Aug-2020
- Publisher
- KOREA INFORMATION PROCESSING SOC
- Keywords
- Blockchain; Digital Evidence Management; Digital Forensic; Hyperledger Fabric; Smart Contract
- Citation
- JOURNAL OF INFORMATION PROCESSING SYSTEMS, v.16, no.4, pp 760 - 773
- Pages
- 14
- Indexed
- SCOPUS
ESCI
KCI
- Journal Title
- JOURNAL OF INFORMATION PROCESSING SYSTEMS
- Volume
- 16
- Number
- 4
- Start Page
- 760
- End Page
- 773
- URI
- https://scholarworks.dongguk.edu/handle/sw.dongguk/6378
- DOI
- 10.3745/JIPS.04.0178
- ISSN
- 1976-913X
2092-805X
- Abstract
- When a crime occurs, the information necessary for solving the case, and various pieces of the evidence needed to prove the crime are collected from the crime scene. The tangible residues collected through scientific methods at the crime scene become evidence at trial and a clue to prove the facts directly against the offense of the suspect. Therefore, the scientific investigation and forensic handling for securing objective forensic in crime investigation is increasingly important. Today, digital systems, such as smartphones, CCTVs, black boxes, etc. are increasingly used as criminal information investigation clues, and digital forensic is becoming a decisive factor in investigation and trial. However, the systems have the risk that digital forensic may be damaged or manipulated by malicious insiders in the existing centralized management systems based on client/server structure. In this paper, we design and implement a blockchain based digital forensic management model using Hyperledger Fabric and Docker to guarantee the reliability and integrity of digital forensic. The proposed digital evidence management model allows only authorized participants in a distributed environment without a central management agency access the network to share and manage potential crime data. Therefore, it could be relatively safe from malicious internal attackers compared to the existing client/server model.
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- Appears in
Collections - College of Advanced Convergence Engineering > Department of Computer Science and Artificial Intelligence > 1. Journal Articles

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