Dashboard Structural Analysis cloak.business SD1 LINKABILITY Case Study
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cloak.business SD1 LINKABILITY
Case Study 5 of 30

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.

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 names, email addresses, phone numbers, social media handles, organizational affiliations. 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

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+, Tauri/Rust) processes documents locally. Combined with zero-storage server architecture, PII is processed and immediately discarded.

Compliance Mapping

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

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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