Dashboard Structural Analysis cloak.business SD2 IRREVERSIBILITY Case Study
← Previous Next →
cloak.business SD2 IRREVERSIBILITY
Case Study 16 of 30

De-Identification of Facial Features in Magnetic Resonance Images: Software Development Using Deep Learning Technology

Jeong, Yeon Uk, Yoo, Soyoung, Kim, Young-Hak et al. · Journal of Medical Internet Research (2020)

Research Source

De-Identification of Facial Features in Magnetic Resonance Images: Software Development Using Deep Learning Technology
Jeong, Yeon Uk, Yoo, Soyoung, Kim, Young-Hak et al. · Journal of Medical Internet Research · 2020 · Source: doaj

BackgroundHigh-resolution medical images that include facial regions can be used to recognize the subject’s face when reconstructing 3-dimensional (3D)-rendered images from 2-dimensional (2D) sequential images, which might constitute a risk of infringement of personal information when sharing data.

Executive Summary

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

cloak.business addresses this through zero-storage microservices processing all data in-memory with no disk writes — PII cannot propagate from a system that never stores it.

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 cloak.business Addresses This

Detection Capabilities

cloak.business identifies 390+ entity types including names, emails, phone numbers, medical records, training data with PII. 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

Replace is recommended for this pain point: substituting PII in training data with realistic synthetic alternatives preserves statistical properties while preventing memorization. Redact provides an alternative — removing PII entirely from training data eliminates memorization risk at the cost of reduced training diversity. For scenarios requiring reversibility, Encrypt (AES-256-GCM) enables authorized recovery of original values.

Architecture & Deployment

Anonymizing training data before ML pipelines prevents PII memorization. The 390+ entity types with 317 custom regex patterns provide the most comprehensive coverage for training data decontamination.

Compliance Mapping

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

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
← Previous Next →