An insightful Machine Learning based Privacy-Preserving Technique for Federated Learning
Research Source
<jats:p>Federated Learning has emerged as a promising paradigm for collaborative machine learning while preserving data privacy. Federated Learning is a technique that enables a large number of users to jointly learn a shared machine learning model, managed by a centralized server while training…
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.
anonym.plus addresses this through 200+ entity types with multi-layer detection accessible across Desktop App (Windows/macOS/Linux) and additional platforms.
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 anonym.plus Addresses This
Detection Capabilities
anonym.plus identifies 200+ entity types including names, emails, SSNs, IBANs, passports, medical records, and country-specific identifiers. The local Presidio 2.2.357 + spaCy 3.8.11 architecture runs entirely offline — no cloud uploads, no internet required for detection. Supports 38 OCR languages via Tesseract for image anonymization.
Anonymization Methods
Redact is recommended for this pain point: completely removing fingerprint-contributing values eliminates the data points that algorithms combine into unique identifiers. Replace provides an alternative — substituting with non-unique alternatives prevents cross-device correlation while preserving document readability. For scenarios requiring reversibility, Encrypt (AES-256-GCM) enables authorized recovery of original values.
Architecture & Deployment
The REST API (Basic plan+) provides programmatic PII detection with Bearer token auth — the most accessible API entry point in the ecosystem.
Compliance Mapping
This pain point intersects with GDPR Article 5(1)(c) data minimization, ePrivacy Directive tracking consent.
anonym.plus's GDPR, HIPAA compliance coverage, combined with Fully offline — no server hosting, provides documented technical measures organizations can reference in their compliance documentation and regulatory submissions.
Product Specifications
| Specification | Value |
|---|---|
| Platform Version | 1.0.0 (desktop) |
| Entity Types | 200+ |
| Accuracy | 95%+ (offline NLP) |
| Languages | 38 (OCR), 20+ (NLP) |
| Anonymization Methods | Replace, Redact, Mask, Hash (SHA-256), Encrypt (AES-256-GCM reversible) |
| Platforms | Desktop App (Windows/macOS/Linux) |
| Pricing | Free, Basic €149, Pro €399, Expert €499 (one-time lifetime) |
| Hosting | Fully offline — no server |
| Compliance | GDPR, HIPAA |