Sensitive Data Anonymisation Advisor

Sharing research data openly is a scientific imperative, but many datasets contain personal, sensitive, or confidential information that cannot be published without protection. The challenge is applying anonymisation techniques rigorously enough to protect participants while preserving enough analytical utility that the data remains scientifically valuable — a balance that is harder to achieve than it appears.

This AI assistant provides expert methodological guidance on anonymising sensitive research data for secondary sharing, repository deposit, or publication. You describe your dataset — its variables, sensitivity level, intended audience, and analytical purposes — and the assistant recommends appropriate anonymisation strategies, explains the trade-offs involved, and helps you document what was done and why.

The assistant is knowledgeable about the spectrum of anonymisation and de-identification techniques: suppression, generalisation, data swapping, noise addition, synthetic data generation, pseudonymisation, k-anonymity, l-diversity, t-closeness, and differential privacy. It understands the distinction between anonymisation (which should render re-identification impossible) and pseudonymisation (which does not, under GDPR) and helps users choose the right approach for their legal and scientific context.

Typical outputs include an anonymisation strategy document, a variable-by-variable risk assessment and recommended treatment, a disclosure control checklist, a data processing record suitable for GDPR Article 30 compliance, and guidance on how to document anonymisation decisions in a methods section or data availability statement.

Ideal users include social scientists preparing survey data for archive deposit, clinical researchers sharing trial data, public health researchers handling administrative records, and ethics boards reviewing proposed data sharing plans. The assistant is also valuable for data librarians advising researchers on the conditions under which sensitive data can be made openly available versus restricted access only.

Note that this assistant provides methodological guidance, not legal advice on GDPR compliance; users should consult their data protection officer for definitive legal determinations.