anonym.plus
Licensed desktop — v8.10.5
30
Case Studies
3
Drivers
3
Solid
0
Structural Limits
200+
Entity Types
SD1
LINKABILITY
SOLID
The core technical problem the ecosystem solves. The anonymize.solutions platform provides a dual-layer detection engine: Layer 1 — 210+ regex recognizers (246 patterns, 75+ country formats, checksum-validated) for deterministic PII; Layer 2 — spaCy (25 langs) + Stanza (7 langs) + XLM-RoBERTa (16 langs) for probabilistic NER. Then 5 anonymization methods break the link: Replace, Redact, Mask, Hash, Encrypt (AES-256-GCM). 260+ entity types across 48 languages — each one a linkability-breaking operation.
- 01 TÉCNICAS PARA ANONIMIZAR DADOS SENSÍVEIS EM SISTEMAS DE INFORMAÇÃO
- 02 Autononym: Multimodal Anonymization of Health Data using Named Entity Recognition and Structured Medical Data Processing
- 03 OpenAIRE webinar - Amnesia: High-accuracy Data Anonymization
- 04 Anonymizing Machine Learning Models
- 05 Towards formalizing the GDPR's notion of singling out.
- 06 From t-closeness to differential privacy and vice versa in data anonymization
- 07 A Survey on Current Trends and Recent Advances in Text Anonymization
- 08 Reconsidering Anonymization-Related Concepts and the Term “Identification” Against the Backdrop of the European Legal Framework
- 09 The lawfulness of re-identification under data protection law
- 10 Blinded Anonymization: a method for evaluating cancer prevention programs under restrictive data protection regulations
SD2
IRREVERSIBILITY
SOLID
If PII is never collected server-side, there is nothing to propagate. cloak.business runs 100% air-gapped with local NLP models — PII never touches a network. anonym.plus processes via local Presidio sidecar with Ed25519 machine-bound licensing. The architecture makes irreversibility structurally impossible — you cannot leak what you never collected.
- 01 GDPR and Large Language Models: Technical and Legal Obstacles
- 02 Balancing AI Innovation and Privacy: A Study of Facial Recognition Technologies under the DPDPA
- 03 A Formal Model for Integrating Consent Management Into MLOps
- 04 GDPR Safeguards for Facial Recognition Technology: A Critical Analysis
- 05 Comparative Analysis of Passkeys (FIDO2 Authentication) on Android and iOS for GDPR Compliance in Biometric Data Protection
- 06 De-Identification of Facial Features in Magnetic Resonance Images: Software Development Using Deep Learning Technology
- 07 Privacy in Italian Clinical Reports: A NLP-Based Anonymization Approach
- 08 Clinical de-identification using sub-document analysis and ELECTRA
- 09 DICOM De-Identification via Hybrid AI and Rule-Based Framework for Scalable, Uncertainty-Aware Redaction
- 10 GDPR Fine: Mercadona S.A. — Spanish Data Protection Authority (aepd) (Spain)
SD5
COMPLEXITY CASCADE
SOLID
anonymize.solutions offers 3 tiers that each eliminate different layers from the attack surface: Self-Managed (Docker, air-gapped) removes cloud dependency. Managed Private (EU infrastructure, customer key mgmt) removes shared-tenancy risk. Online SaaS minimizes deployment complexity. Plus 6 integration points each operating at a different layer.
- 01 Systematic review of privacy-preserving Federated Learning in decentralized healthcare systems
- 02 [Anonymization of general practitioners' electronic medical records in two research datasets].
- 03 A Comprehensive Evaluation of Privacy-Preserving Mechanisms in Cloud-Based Big Data Analytics: Challenges and Future Research Directions
- 04 Privacy Risk Assessment Frameworks for Large-Scale Medical Datasets Using Computational Metrics
- 05 Data Obfuscation Through Latent Space Projection for Privacy-Preserving AI Governance: Case Studies in Medical Diagnosis and Finance Fraud Detection
- 06 Turkish data protection law: GDPR alignment and key 2024 amendment
- 07 AI Meets Anonymity: How named entity recognition is redefining data privacy
- 08 Viewing the GDPR through a de-identification lens: a tool for compliance, clarification, and consistency
- 09 Mitigating AI risks: A comparative analysis of Data Protection Impact Assessments under GDPR and KVKK
- 10 Approaches for Anonymization Methods in IoT Preservation Privacy
| Product Specifications | |
|---|---|
| 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) |
Other Product Case Studies
Research Basis
Case studies on this page are grounded in peer-reviewed research. A sample of foundational papers:
- Fracacio & Dallilo (2025). Técnicas para Anonimizar Dados Sensíveis em Sistemas de Informação.
- Yalic et al. (2025). Autononym: Multimodal Anonymization of Health Data using Named Entity Recognition.
- Terrovitis (2023). OpenAIRE Amnesia: High-accuracy Data Anonymization.
Full citation metadata available in each case study page JSON-LD.
Considerations
Not for everyone: This solution is best suited for organizations with stringent compliance requirements (GDPR, HIPAA, CCPA, SOC 2). Smaller teams without dedicated privacy resources may find simpler tools more appropriate for their use case.
Training investment: Enterprise deployment requires 2-4 weeks of team training to configure entity patterns, establish workflows, and integrate with existing systems. Success depends on dedicated privacy engineering resources.