Regulatory compliance through Pseudonymisation or Anonymisation
Accelerate your path towards regulatory compliance, data sharing and privacy protection. Based on ML6’s proven approach, we guide you through your complex pseudonymisation or anonymisation use case while reducing cost and development time through reusable components.
Business Value
Pseudonymization and anonymisation are powerful measures to protect personal data, and often necessary to ensure compliance with data regulations, enable data sharing, mitigate risks and foster transparency and trust.
Pseudonymisation and anonymisation are two distinct techniques used to protect personal data while still allowing its use for various purposes. Which technique to use depends on the specific use case and applicable regulation.
1. Pseudonymisation - is a data processing technique where personally identifiable information (PII) is replaced with pseudonyms or artificial identifiers. The purpose is to prevent direct identification of individuals while retaining the possibility of re-identifying individuals with the help of additional data, typically held separately, and the process is reversible.
2. Anonymisation - is an irreversible process that transforms personal data into a state where it can no longer be attributed to an identifiable individual, even through the use of additional information, eliminating any risk of re-identification. Anonymised data does not fall under the scope of data protection regulations such as GDPR, as it is no longer considered personal data.
Transparency obligations in the public sector
Efficiently pseudonomize documents that need to be openly published to comply with regulations such as CeReBro, Digital Transformation of Justice plan or Wet Open Overheid (WOO) and create transparency towards citizens.
GDPR compliance of cloud usage
Comply with the General Data Protection Regulation (GDPR) by anonymizing your data when keeping it in the cloud. We guide you through the process so that you can focus on creating the most value from your data without legal risks.
LLM training in regulated environments
Ensure data privacy and mitigate risks of re-identification by anonymizing data used for training or fine-tuning Large Language Models (LLMs) on your specific enterprise data containing personal information.
Tackling your complex use cases with a proven approach
1. Define - In tailored workshops, we together define the entities that need to be pseudonomized or anonymized
2. Extract - We extract the relevant entities (whether standard or custom entities) from your documents to detect the personal data of your needs.
3. Randomize - We pseudonomise or anonymise, depending on your specific needs, the detected personal data and replace each entity with statistically equivalent data to guarantee privacy and confidentiality
4. Visualize & Validate - We offer a web application to visualize the outcomes to users and allow for user validation, integrated into your existing workflow
5. Improve / Maintain - We maintain and support improvements of your pseudonymisation solution, enabling access to new features and capabilities and training of new models.
Technical and functional specifications
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Multi-cloud
Azure
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Contact our experts
Discover how our solution can support you in your complex anonymisation or pseudonymisation use cases. We're here to answer your questions, understand your specific requirements, and guide you on your path to ensuring compliance with data regulations, enabling data sharing, mitigating risks and fostering transparency and trust.
