Dashboard Structural Analysis anonym.plus SD2 IRREVERSIBILITY Case Study
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anonym.plus SD2 IRREVERSIBILITY
Case Study 18 of 30

Clinical de-identification using sub-document analysis and ELECTRA

Rosario Catelli, F. Gargiulo, Emanuele Damiano et al. · International Conference on Digital Health (2021-09-01)

Research Source

Clinical de-identification using sub-document analysis and ELECTRA
Rosario Catelli, F. Gargiulo, Emanuele Damiano et al. · International Conference on Digital Health · 2021-09-01 · Source: semantic_scholar

The privacy protection mechanism in the health context is becoming a crucial task given the exponential increase in the adoption of the Electronic Health Records (EHRs) all around the world. This kind of data can be used for medical investigation and research only if it is filtered out of all the so called Protected Health Information (PHI).

Executive Summary

This research paper examines a critical privacy challenge related to IRREVERSIBILITY — once pii propagates, it cannot be un-propagated.

anonym.plus addresses this through 100% local processing with AES-256-GCM encrypted vault — PII processed and stored locally, never touching any external server.

Root Cause: SD2 — IRREVERSIBILITY

Once PII propagates, it cannot be un-propagated. The arrow of data only points one direction. PII exposure is a one-way function with no inverse.

Irreducible truth: Information entropy only increases. You cannot recall a broadcast signal. You cannot un-train a neural network. You cannot selectively erase a backup tape. Every deletion mechanism is an approximation — and the original exposure persists.

The Solution: How anonym.plus Addresses This

Detection Capabilities

anonym.plus identifies 200+ entity types including email addresses, passwords, usernames, IP addresses, account 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 of credentials in documents enables authorized access for incident response while protecting at rest. Hash provides an alternative — SHA-256 hashing enables breach impact analysis without exposing original values. For permanent removal, Redact ensures data cannot be recovered under any circumstances.

Architecture & Deployment

Zero cloud dependency after activation. Ed25519 machine-bound licensing requires only initial activation — subsequent operations are completely offline. All processing stays local.

Compliance Mapping

This pain point intersects with GDPR Articles 33-34 breach notification, Article 32 security measures.

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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