An investigation of data privacy models focusing on anonymization techniques such as Generalization, Pseudonymization, Suppression, and Perturbation. It details formal models like k-Anonymity, l-Diversity, and t-Closeness, which emerged sequentially to mitigate vulnerabilities and protect Quasi-Identifiers (QIs) and sensitive attributes against linkage and inference attacks.
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 processed 100% locally via Presidio 2.2.357 sidecar — detection and anonymization that never leaves the device.
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.
anonym.plus identifies 200+ entity types including device identifiers, advertising IDs, tracking cookies, user agent strings. 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.
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.
The local sidecar REST API (port 5002-5003) provides programmatic access to Presidio detection for local development workflow integration.
This pain point intersects with GDPR Article 5(1)(c) data minimization, ePrivacy Directive tracking consent.
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.
| Specification | Value |
|---|---|
| App Version | v8.10.5 |
| Entity Types | 200+ built-in, up to 50 custom |
| Detection Engine | Presidio 2.2.357 + spaCy 3.8.11 (23 models) |
| Languages | 48 UI, 23 NLP models |
| Document Formats | PDF, DOCX, XLSX, TXT, CSV, JSON, XML + Image OCR |
| Anonymization Methods | Replace, Redact, Mask, Hash (SHA-256/512/MD5), Encrypt (AES-256-GCM) |
| Architecture | Tauri 2.x (Rust + React) + FastAPI sidecar (~370 MB) |
| Platforms | Win/Mac/Linux |
| Licensing | Ed25519 signed, machine-fingerprinted, max 5 machines |
| Processing | 100% local — data never leaves device |
| Compliance | GDPR, HIPAA (data residency guaranteed by local processing) |