Dashboard Structural Analysis anonym.legal SD1 LINKABILITY Case Study
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anonym.legal SD1 LINKABILITY
Case Study 5 of 40

Towards formalizing the GDPR's notion of singling out.

Cohen, Aloni, Nissim, Kobbi · Proceedings of the National Academy of Sciences of the United States of America (2020-03-31)

Research Source

Towards formalizing the GDPR's notion of singling out.
Cohen, Aloni, Nissim, Kobbi · Proceedings of the National Academy of Sciences of the United States of America · 2020-03-31 · Source: pubmed

There is a significant conceptual gap between legal and mathematical thinking around data privacy. The effect is uncertainty as to which technical offerings meet legal standards. This uncertainty is exacerbated by a litany of successful privacy attacks demonstrating that traditional statistical disclosure limitation techniques often fall short of the privacy envisioned by regulators.

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 names, email addresses, phone numbers, social media handles, organizational affiliations. 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: removing contact identifiers from documents prevents construction of social graphs from document collections. Replace provides an alternative — substituting names and identifiers with type labels preserves document structure while breaking the social graph. For scenarios requiring reversibility, Encrypt (AES-256-GCM) enables authorized recovery of original values.

Architecture & Deployment

The Desktop App (Windows 10+, macOS 10.15+, Ubuntu 20.04+) processes files locally with encrypted vault storage (AES-256-GCM). Files never uploaded — only extracted text is processed.

Compliance Mapping

This pain point intersects with GDPR Article 5(1)(c) data minimization, Article 25 data protection by design.

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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