Dashboard Structural Analysis anonym.legal SD1 LINKABILITY Case Study
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anonym.legal SD1 LINKABILITY
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A Survey on Current Trends and Recent Advances in Text Anonymization

Tobias Deußer, Lorenz Sparrenberg, Armin Berger et al. · International Conference on Data Science and Advanced Analytics (2025-08-29)

Research Source

A Survey on Current Trends and Recent Advances in Text Anonymization
Tobias Deußer, Lorenz Sparrenberg, Armin Berger et al. · International Conference on Data Science and Advanced Analytics · 2025-08-29 · Source: semantic_scholar

The proliferation of textual data containing sensitive personal information across various domains requires robust anonymization techniques to protect privacy and comply with regulations, while preserving data usability for diverse and crucial downstream tasks. This survey provides a comprehen-sive overview of current trends and recent advances in text anonymization techniques.

Executive Summary

This research paper examines a critical privacy challenge related to LINKABILITY — the ability to connect two pieces of information to the same person.

anonym.legal addresses this through 260+ entity types with 3-layer hybrid detection accessible via 6 platforms including Chrome Extension for real-time browser anonymization.

Root Cause: SD1 — LINKABILITY

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.

The Solution: How anonym.legal Addresses This

Detection Capabilities

anonym.legal identifies 260+ entity types including MAC addresses, device serial numbers, CPU identifiers, TPM keys, hardware UUIDs. The 3-layer hybrid (Presidio + NLP + Stance classification) architecture uses Microsoft Presidio deterministic rules with checksum validations (Luhn, RFC-822) for structured identifiers and XLM-RoBERTa + Stanza NER with Stance classification for disambiguation for contextual references.

Anonymization Methods

Redact is recommended for this pain point: completely removing hardware identifiers from documents and logs eliminates persistent tracking anchors that survive OS reinstalls. Hash provides an alternative — hashing hardware identifiers enables device-level analytics without exposing actual serial numbers. For scenarios requiring reversibility, Encrypt (AES-256-GCM) enables authorized recovery of original values.

Architecture & Deployment

The REST API (Basic plan+, €3/month) provides programmatic PII detection with Bearer token auth. Rate limited to 100 req/min, max 100 KB per request — the most accessible API entry point in the ecosystem.

Compliance Mapping

This pain point intersects with GDPR Article 4(1) device identifiers as personal data, ePrivacy Article 5(3).

anonym.legal’s GDPR, HIPAA, PCI-DSS, ISO 27001 compliance coverage, combined with Hetzner Germany, ISO 27001 certified hosting, provides documented technical measures organizations can reference in their compliance documentation and regulatory submissions.

Product Specifications

SpecificationValue
Platform Versionv7.4.4
Entity Types260+
Detection Layers3-layer: Presidio + NLP + Stance classification
Accuracy95.5% tested (42/44 tests)
Languages48
Anonymization MethodsReplace, Redact, Mask, Hash (SHA-256/512/MD5), Encrypt (AES-256-GCM)
PlatformsWeb App, Desktop, Office Add-in, MCP Server, Chrome Extension, REST API
PricingFree €0, Basic €3, Pro €15, Business €29
HostingHetzner Germany, ISO 27001
ComplianceGDPR, HIPAA, PCI-DSS, ISO 27001
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