Add Synthetic Data Disclosure to README#66
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Code Review
This pull request updates the README.md to include a 'Privacy-by-design' pillar and a new 'Synthetic Data Disclosure' section, clarifying the use of synthetic data to maintain a privacy-by-design posture. The review feedback identifies two opportunities to improve consistency with the project's style guide: standardizing the term 'Privacy by design' by removing hyphens and ensuring that both GDPR and DSGVO are explicitly referenced in compliance statements.
| | Deterministic transport | The same reviewed input is expected to produce stable KVTC-V7 frame structure under the same code revision. | | ||
| | Audit-friendly artifacts | Reports, schemas, compact summaries, and uploaded CI artifacts provide reviewable evidence. | | ||
| | Synthetic-only posture | Examples and validation fixtures are synthetic/static; no real Daimler, customer, fleet, or production payloads are claimed. | | ||
| | Privacy-by-design | Public examples avoid personal data, VIN-linked datasets, production telemetry, and private enterprise logs by design. | |
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The repository style guide (Line 10) defines this pillar as Privacy by design (without hyphens). For consistency with the project's architectural definitions, consider removing the hyphens in this table entry.
| | Privacy-by-design | Public examples avoid personal data, VIN-linked datasets, production telemetry, and private enterprise logs by design. | | |
| | Privacy by design | Public examples avoid personal data, VIN-linked datasets, production telemetry, and private enterprise logs by design. | |
References
- Privacy by design aligned with GDPR / DSGVO Art. 25 (link)
| - VIN-linked datasets; | ||
| - private enterprise logs. | ||
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| Synthetic data is used to keep the project reviewable under a privacy-by-design posture aligned with GDPR Art. 25 principles. It also supports reproducible validation, deterministic CI artifacts, and safe cloud-based review without exposing customer, fleet, or enterprise operational records. |
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To align with the Security pillar in the repository style guide (Line 10), please reference both GDPR and DSGVO. This ensures the documentation accurately reflects the project's compliance posture in the Daimler Trucks / Industry 4.0 context.
| Synthetic data is used to keep the project reviewable under a privacy-by-design posture aligned with GDPR Art. 25 principles. It also supports reproducible validation, deterministic CI artifacts, and safe cloud-based review without exposing customer, fleet, or enterprise operational records. | |
| Synthetic data is used to keep the project reviewable under a privacy-by-design posture aligned with GDPR / DSGVO Art. 25 principles. It also supports reproducible validation, deterministic CI artifacts, and safe cloud-based review without exposing customer, fleet, or enterprise operational records. |
References
- Privacy by design aligned with GDPR / DSGVO Art. 25 (link)
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Motivation
Description
## 📊 Synthetic Data Disclosuresection near the architecture/validation area that explicitly states the repository does not include proprietary customer data, production telemetry, VIN-linked datasets, or private enterprise logs.Limitationssubsection clarifying that synthetic data is not full real-world fidelity and that production deployment requires controlled calibration against approved enterprise datasets.Privacy-by-designrow to the architecture highlights table for improved reviewer clarity.Testing
git diff --check(diff/whitespace validation) and it passed successfully.Codex Task