Dashboard Structural Analysis anonym.plus SD5 COMPLEXITY CASCADE Case Study
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anonym.plus SD5 COMPLEXITY CASCADE
Case Study 22 of 30

[Anonymization of general practitioners' electronic medical records in two research datasets].

Hauswaldt J, Groh R, Kaulke K et al. · Das Gesundheitswesen (2025-07-14)

Research Source

[Anonymization of general practitioners' electronic medical records in two research datasets].
Hauswaldt J, Groh R, Kaulke K et al. · Das Gesundheitswesen · 2025-07-14 · Source: europe_pmc

A dataset can be called "anonymous" only if its content cannot be related to a person, not by any means and not even ex post or by combination with other information. Free text entries highly impede "factual anonymization" for secondary research.

Executive Summary

This research paper examines a critical privacy challenge related to COMPLEXITY CASCADE — pii protection requires perfection across all layers simultaneously.

anonym.plus addresses this through 100% local processing eliminating cloud, network, and third-party layers, reducing the attack surface to the local device.

Root Cause: SD5 — COMPLEXITY CASCADE

PII protection requires perfection across ALL layers simultaneously. One failure anywhere collapses everything. The attacker needs to find ONE weakness; the defender must protect ALL layers with zero failures.

Irreducible truth: Protection = Layer1 × Layer2 × ... × LayerN. Any zero makes the product zero. The attacker gets to choose which layer to attack. The defender must achieve perfection across all of them simultaneously, forever.

The Solution: How anonym.plus Addresses This

Detection Capabilities

anonym.plus identifies 200+ entity types including message content, contact names, conversation metadata, attachment identifiers. The local Presidio 2.2.357 + spaCy 3.8.11 architecture uses Presidio 2.2.357 deterministic recognizers with 121 built-in presets for structured identifiers and spaCy 3.8.11 with 23 language models, all running locally via FastAPI sidecar for contextual references.

Anonymization Methods

Encrypt is recommended for this pain point: AES-256-GCM encryption in backups provides protection that persists even if backup systems lack encryption. Redact provides an alternative — removing PII from messages before backup prevents unencrypted-backup exposure regardless of backup encryption status. For permanent removal, Redact ensures data cannot be recovered under any circumstances.

Architecture & Deployment

100% local processing — data never leaves the device. Presidio 2.2.357 sidecar runs all detection locally with spaCy 3.8.11 (23 models). After activation, fully offline operation.

Compliance Mapping

This pain point intersects with GDPR Article 32 encryption as security measure, Article 5(1)(f) confidentiality.

anonym.plus’s GDPR (data never leaves device), HIPAA (local processing) compliance coverage, combined with 100% local — data never leaves device hosting, provides documented technical measures organizations can reference in their compliance documentation and regulatory submissions.

Product Specifications

SpecificationValue
App Versionv8.10.5
Entity Types200+ built-in, up to 50 custom
Detection EnginePresidio 2.2.357 + spaCy 3.8.11 (23 models)
Languages48 UI, 23 NLP models
Document FormatsPDF, DOCX, XLSX, TXT, CSV, JSON, XML + Image OCR
Anonymization MethodsReplace, Redact, Mask, Hash (SHA-256/512/MD5), Encrypt (AES-256-GCM)
ArchitectureTauri 2.x (Rust + React) + FastAPI sidecar (~370 MB)
PlatformsWin/Mac/Linux
LicensingEd25519 signed, machine-fingerprinted, max 5 machines
Processing100% local — data never leaves device
ComplianceGDPR, HIPAA (data residency guaranteed by local processing)
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