Dashboard Structural Analysis cloak.business SD1 LINKABILITY Case Study
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cloak.business SD1 LINKABILITY
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Autononym: Multimodal Anonymization of Health Data using Named Entity Recognition and Structured Medical Data Processing

Hamdi Yalin Yalic, Murat Dörterler, Alaettin Uçan et al. · Medical Technologies National Conference (2025-10-26)

Research Source

Autononym: Multimodal Anonymization of Health Data using Named Entity Recognition and Structured Medical Data Processing
Hamdi Yalin Yalic, Murat Dörterler, Alaettin Uçan et al. · Medical Technologies National Conference · 2025-10-26 · Source: semantic_scholar

This paper presents Autononym, an AI-powered software platform capable of robustly and scalably anonymizing health data across several formats, including unstructured free-text documents, tabular datasets, and medical images in both DICOM and standard RGB formats.

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.

cloak.business addresses this through 390+ entity types with 317 custom regex recognizers, processed in-memory on German servers with zero third-party data sharing.

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 cloak.business Addresses This

Detection Capabilities

cloak.business identifies 390+ entity types including zip codes, dates of birth, gender markers, demographic quasi-identifiers. The dual-layer (317 custom regex + NLP) architecture uses 317 custom regex recognizers with context word analysis and confidence scoring 0.0–1.0 for structured identifiers and spaCy (25 languages) + Stanza (7 languages) + XLM-RoBERTa (16 languages) — all self-hosted for contextual references.

Anonymization Methods

Hash is recommended for this pain point: deterministic SHA-256 hashing enables referential integrity across datasets while preventing re-identification from original values. Replace provides an alternative — substituting quasi-identifiers with type labels removes re-identification potential while preserving data structure. For scenarios requiring reversibility, Encrypt (AES-256-GCM) enables authorized recovery of original values.

Architecture & Deployment

The REST API (Business plan) provides programmatic access to 317 custom regex recognizers and 3 NLP engines. Session-based JWT auth for web/desktop; Bearer API key for MCP/REST integration.

Compliance Mapping

This pain point intersects with GDPR Recital 26 identifiability test, Article 89 research safeguards.

cloak.business’s GDPR (Article 25 Privacy by Design), ISO 27001:2022 compliance coverage, combined with Germany only, no third-party transfers, ISO 27001:2022 certified hosting, provides documented technical measures organizations can reference in their compliance documentation and regulatory submissions.

Product Specifications

SpecificationValue
Platform VersionAnalyzer 6.9.1, Image Redactor 5.3.0
Entity Types390+ (519 documented)
Detection Layers317 custom regex + 3 NLP engines (all self-hosted)
Languages48 UI languages, 37 OCR language packs
Anonymization MethodsReplace, Redact, Mask, Hash (SHA-256), Encrypt (AES-256-GCM)
ArchitectureZero-storage microservices (in-memory only)
Integration PointsWeb App, Desktop, Office Add-in, MCP Server (9 tools), REST API
HostingGermany only, ISO 27001:2022, no third-party transfers
ComplianceGDPR Article 25, ISO 27001:2022
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