Post
74
✅ New Guide: GDPR-Compatible "Ethical Redaction" (v0.1)
Title:
🔒 GDPR‑Compatible “Ethical Redaction” on Conventional Stacks — Guide (v0.1)
🔗 https://huggingface.co/blog/kanaria007/gdpr-ethical-redaction-v0-1
---
Summary:
The "right to erasure" often breaks on real stacks: backups, replicas, caches,
vendor processors—and especially ML training pipelines and feature stores.
This guide shows how to make erasure operational using crypto-shredding + WORM
audit and an Erasure Orchestrator covering databases, objects, search, BI,
caches, vector DBs/models, and more.
> Privacy compliance without losing explainability.
> Deployable on conventional infra, now.
---
Why It Matters:
- *Backups included by design* (key destruction = erasure across all copies)
- *ML-aware*: track subject_ref through pipelines; handle model disable/retrain/output-guard
- *Provable evidence*: anonymous tombstones + WORM logs, no raw IDs
- *Measurable compliance*: p95 SLAs, coverage %, re-ID risk—tracked continuously
---
What's Inside:
*Foundation:*
KMS hierarchy, WORM audit, Erasure Orchestrator; state machine (verify → crypto-shred → propagate); API endpoints for all GDPR rights
*Coverage:*
All data products (RDBMS/objects/search/BI/caches/backups); ML specifics (§12): subject_ref lineage + erasure strategies; processor propagation (Art.28)
*Compliance:*
14 KPIs with SLA targets + automated probes; special categories & minors safeguards; multi-region KMS; migration playbook + runbooks
---
📖 Informative Engineering Guide
Legal mapping: Arts. 5, 15–22, 25, 28 + Recital 26 → deployable patterns
Not legal advice. Text: CC BY 4.0. Code: MIT.
---
For production AI/ML stacks, this provides recipes, SLAs, and evidence models to ship privacy-by-design.
Title:
🔒 GDPR‑Compatible “Ethical Redaction” on Conventional Stacks — Guide (v0.1)
🔗 https://huggingface.co/blog/kanaria007/gdpr-ethical-redaction-v0-1
---
Summary:
The "right to erasure" often breaks on real stacks: backups, replicas, caches,
vendor processors—and especially ML training pipelines and feature stores.
This guide shows how to make erasure operational using crypto-shredding + WORM
audit and an Erasure Orchestrator covering databases, objects, search, BI,
caches, vector DBs/models, and more.
> Privacy compliance without losing explainability.
> Deployable on conventional infra, now.
---
Why It Matters:
- *Backups included by design* (key destruction = erasure across all copies)
- *ML-aware*: track subject_ref through pipelines; handle model disable/retrain/output-guard
- *Provable evidence*: anonymous tombstones + WORM logs, no raw IDs
- *Measurable compliance*: p95 SLAs, coverage %, re-ID risk—tracked continuously
---
What's Inside:
*Foundation:*
KMS hierarchy, WORM audit, Erasure Orchestrator; state machine (verify → crypto-shred → propagate); API endpoints for all GDPR rights
*Coverage:*
All data products (RDBMS/objects/search/BI/caches/backups); ML specifics (§12): subject_ref lineage + erasure strategies; processor propagation (Art.28)
*Compliance:*
14 KPIs with SLA targets + automated probes; special categories & minors safeguards; multi-region KMS; migration playbook + runbooks
---
📖 Informative Engineering Guide
Legal mapping: Arts. 5, 15–22, 25, 28 + Recital 26 → deployable patterns
Not legal advice. Text: CC BY 4.0. Code: MIT.
---
For production AI/ML stacks, this provides recipes, SLAs, and evidence models to ship privacy-by-design.