anonymize.solutions
Umbrella platform — v1.6.12
40Case Studies
4Drivers
3Solid
1Structural Limits
260+Entity Types
SD1
LINKABILITY
SOLIDThe 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.
- 01TÉCNICAS PARA ANONIMIZAR DADOS SENSÍVEIS EM SISTEMAS DE INFORMAÇÃO
- 02Autononym: Multimodal Anonymization of Health Data using Named Entity Recognition and Structured Medical Data Processing
- 03OpenAIRE webinar - Amnesia: High-accuracy Data Anonymization
- 04Anonymizing Machine Learning Models
- 05Towards formalizing the GDPR's notion of singling out.
- 06From t-closeness to differential privacy and vice versa in data anonymization
- 07A Survey on Current Trends and Recent Advances in Text Anonymization
- 08Reconsidering Anonymization-Related Concepts and the Term “Identification” Against the Backdrop of the European Legal Framework
- 09The lawfulness of re-identification under data protection law
- 10Blinded Anonymization: a method for evaluating cancer prevention programs under restrictive data protection regulations
SD5
COMPLEXITY CASCADE
SOLIDanonymize.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.
- 01Systematic review of privacy-preserving Federated Learning in decentralized healthcare systems
- 02[Anonymization of general practitioners' electronic medical records in two research datasets].
- 03A Comprehensive Evaluation of Privacy-Preserving Mechanisms in Cloud-Based Big Data Analytics: Challenges and Future Research Directions
- 04Privacy Risk Assessment Frameworks for Large-Scale Medical Datasets Using Computational Metrics
- 05Data Obfuscation Through Latent Space Projection for Privacy-Preserving AI Governance: Case Studies in Medical Diagnosis and Finance Fraud Detection
- 06Turkish data protection law: GDPR alignment and key 2024 amendment
- 07AI Meets Anonymity: How named entity recognition is redefining data privacy
- 08Viewing the GDPR through a de-identification lens: a tool for compliance, clarification, and consistency
- 09Mitigating AI risks: A comparative analysis of Data Protection Impact Assessments under GDPR and KVKK
- 10Approaches for Anonymization Methods in IoT Preservation Privacy
SD6
KNOWLEDGE ASYMMETRY
SOLIDanonymize.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.
- 01Slave to the Algorithm? Why a 'right to an explanation' is probably not the remedy you are looking for
- 02Internet of Things and Blockchain: Legal Issues and Privacy. The Challenge for a Privacy Standard
- 03The Internet of Things ecosystem: The blockchain and privacy issues. The challenge for a global privacy standard
- 04Data Protection Issues for Smart Contracts
- 05Article 39 Tasks of the data protection officer
- 06Article 38 Position of the data protection officer
- 07Balancing Security and Privacy: Web Bot Detection, Privacy Challenges, and Regulatory Compliance under the GDPR and AI Act.
- 08GDPR’s reflection in privacy-enhancing technologies : implications for AI data protection
- 09Experiential case study audit of three popular period trackers using General Data Protection Regulation (GDPR) and intimate privacy assessment criteria.
- 10AI Ethics: Algorithmic Determinism or Self-Determination? The GPDR Approach
SD7
JURISDICTION FRAGMENTATION
STRUCTURAL LIMITNo 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.
- 01Structuring AI Risk Management Framework: EU AI Act FRIA, GDPR DPIA and ISO 42001/23894
- 02TRANSATLANTIC DATA TRANSFER COMPLIANCE (28 B.U. J. SCI. & TECH. L. 158 (2022))
- 03Affective Computing and Emotional Data: Challenges and Implications in Privacy Regulations, The AI Act, and Ethics in Large Language Models
- 04Identification and assessment of eligibility criteria for preparing the Personal Data Protection Impact Assessment (RIPD)
- 05The global impact of the General Data Protection Regulation: implications, challenges, and future outlook in oncology clinical research sponsors.
- 06Processing Data to Protect Data: Resolving the Breach Detection Paradox
- 07Enhancing AI fairness through impact assessment in the European Union: a legal and computer science perspective
- 08Standard contractual clauses for cross-border transfers of health data after
- 09Airline Commercial Use of EU Personal Data in the Context of the GDPR, British Airways and Schrems II
- 10GDPR 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.