xCRM: Blockchain Interoperable Crime Report Management System By Utilizing Hyperledger Cacti & Private Data Collection (PDC)
Bibliographic record
Abstract
The process of bringing criminals to justice can be complicated when reporter information and sensitive data related to the case are revealed and may involve international law enforcement cooperation, especially when a criminal flees to another country. To tackle this issue, an interoperable crime management system is necessary. This study proposes a blockchain-based interoperable crime management system that provides secure and decentralized communication between different blockchain-based platforms, ensuring anonymity, transparency, and immutability. We present a methodology for crime reporting, evidence management, forensic testing, a collaboration between investigation agencies, and resource sharing where anyone can report in two modes: anonymous mode, which is only for passing any information to the police, or generate mode, which will create an FIR (First Information Report) and subsequent procedures. To ensure the security of our data, we have implemented Hyperledger Fabric Private Data Collection (PDC) for each report and investigation. The PDC will consist of the team leader, investigation officer, reporter, and any other relevant users as members. All interactions will occur within the specific channel created for each report, and files and additional information will be shared through the PDC. This system uses Hyperledger Cacti to implement interoperability and allows investigation and collaboration with others, like courts, forensics, and special investigation agencies, even with foreign systems. This proposed system is effective and efficient, enhancing the performance of blockchain networks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".