anonymize.solutions
Umbrella platform — v1.6.12
40
Case Studies
4
Drivers
3
Solid
1
Structural Limits
260+
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
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
SD6
KNOWLEDGE ASYMMETRY
SOLID
anonymize.solutions publishes 13 educational resource pages and 10 demo platforms bridging the research-practice gap. The MCP Server (7 tools for Claude Desktop, Cursor, VS Code) embeds PII awareness directly in developer workflows.
- 01 Slave to the Algorithm? Why a 'right to an explanation' is probably not the remedy you are looking for
- 02 Internet of Things and Blockchain: Legal Issues and Privacy. The Challenge for a Privacy Standard
- 03 The Internet of Things ecosystem: The blockchain and privacy issues. The challenge for a global privacy standard
- 04 Data Protection Issues for Smart Contracts
- 05 Article 39 Tasks of the data protection officer
- 06 Article 38 Position of the data protection officer
- 07 Balancing Security and Privacy: Web Bot Detection, Privacy Challenges, and Regulatory Compliance under the GDPR and AI Act.
- 08 GDPR’s reflection in privacy-enhancing technologies : implications for AI data protection
- 09 Experiential case study audit of three popular period trackers using General Data Protection Regulation (GDPR) and intimate privacy assessment criteria.
- 10 AI Ethics: Algorithmic Determinism or Self-Determination? The GPDR Approach
SD7
JURISDICTION FRAGMENTATION
STRUCTURAL LIMIT
No product can harmonize 200 legal systems. But the ecosystem is architected for jurisdictional flexibility: 100% EU hosting satisfies GDPR. Self-Managed Docker satisfies data localization. Compliance spans GDPR, HIPAA, FERPA, PCI-DSS, ISO 27001.
- 01 Structuring AI Risk Management Framework: EU AI Act FRIA, GDPR DPIA and ISO 42001/23894
- 02 TRANSATLANTIC DATA TRANSFER COMPLIANCE (28 B.U. J. SCI. & TECH. L. 158 (2022))
- 03 Affective Computing and Emotional Data: Challenges and Implications in Privacy Regulations, The AI Act, and Ethics in Large Language Models
- 04 Identification and assessment of eligibility criteria for preparing the Personal Data Protection Impact Assessment (RIPD)
- 05 The global impact of the General Data Protection Regulation: implications, challenges, and future outlook in oncology clinical research sponsors.
- 06 Processing Data to Protect Data: Resolving the Breach Detection Paradox
- 07 Enhancing AI fairness through impact assessment in the European Union: a legal and computer science perspective
- 08 Standard contractual clauses for cross-border transfers of health data after
- 09 Airline Commercial Use of EU Personal Data in the Context of the GDPR, British Airways and Schrems II
- 10 GDPR Fine: IAB Europe — Belgian Data Protection Authority (APD) (Belgium)
| Product Specifications | |
|---|---|
| Product Version | v1.6.12 |
| Entity Types | 260+ |
| Detection Layers | Dual-layer: 210+ regex recognizers + 3 NLP engines |
| Languages | 48 (spaCy 25, Stanza 7, XLM-RoBERTa 16) |
| Anonymization Methods | Replace, Redact, Mask, Hash (SHA-256), Encrypt (AES-256-GCM) |
| Deployment Options | SaaS, Managed Private, Self-Managed (Docker/Air-Gapped) |
| Integration Points | REST API, MCP Server, Office Add-in, Desktop App, Chrome Extension |
| Hosting | 100% EU (Hetzner Germany, ISO 27001) |
| Compliance | GDPR, HIPAA, FERPA, PCI-DSS, ISO 27001 |
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.