The webinar will introduce the concept of anonymization of research data, including direct identifiers and quasi-identifiers using Amnesia, which is a flexible data anonymization tool that transforms sensitive data to datasets where formal privacy guarantees hold. Amnesia transforms original data to provide k-anonymity and km-anonymity.
This research paper examines a critical privacy challenge related to LINKABILITY — the ability to connect two pieces of information to the same person.
anonymize.solutions addresses this through dual-layer detection (210+ regex + 3 NLP engines) identifying 260+ entity types across 48 languages, with 5 anonymization methods that break the linkability chain.
The ability to connect two pieces of information to the same person. This is the foundational operation that makes PII dangerous. Nearly every pain point is an expression of linkability being created, exploited, or failing to be broken.
Irreducible truth: You cannot have useful data that is completely unlinkable AND completely useful. The very features that make data informative make it linkable. This is not a bug — it is information theory. The information content of a dataset and its linkability are the same property measured differently.
anonymize.solutions identifies 260+ entity types including email addresses, timestamps, IP addresses, communication metadata, geolocation markers. The dual-layer (regex + NLP) architecture uses 210+ custom pattern recognizers (246 patterns, 75+ country formats, checksum-validated) for structured identifiers and spaCy (25 languages) + Stanza (7 languages) + XLM-RoBERTa (16 languages) for contextual references.
Redact is recommended for this pain point: removing metadata fields entirely prevents correlation attacks that link communication patterns to individuals. Mask provides an alternative — partial masking preserves format for system compatibility while breaking linkability. For scenarios requiring reversibility, Encrypt (AES-256-GCM) enables authorized recovery of original values.
The REST API integrates into data pipelines (n8n, Make, Zapier) for automated PII anonymization before data reaches downstream systems. Three deployment models — SaaS (token pay-per-use), Managed Private (customer key management), and Self-Managed (Docker, air-gapped) — match any infrastructure requirement.
This pain point intersects with GDPR Article 5(1)(f) integrity and confidentiality, ePrivacy Directive metadata restrictions.
anonymize.solutions’s GDPR, HIPAA, FERPA, PCI-DSS, ISO 27001 compliance coverage, combined with 100% EU (Hetzner Germany, ISO 27001) hosting, provides documented technical measures organizations can reference in their compliance documentation and regulatory submissions.
| Specification | Value |
|---|---|
| Product Version | v1.6.12 |
| Entity Types | 260+ |
| Detection Layers | Dual-layer: 210+ regex recognizers + 3 NLP engines |
| Languages | 48 (spaCy 25, Stanza 7, XLM-RoBERTa 16) |
| Anonymization Methods | Replace, Redact, Mask, Hash (SHA-256), Encrypt (AES-256-GCM) |
| Deployment Options | SaaS, Managed Private, Self-Managed (Docker/Air-Gapped) |
| Integration Points | REST API, MCP Server, Office Add-in, Desktop App, Chrome Extension |
| Hosting | 100% EU (Hetzner Germany, ISO 27001) |
| Compliance | GDPR, HIPAA, FERPA, PCI-DSS, ISO 27001 |