Data clean rooms for privacy-safe collaboration

Data clean rooms let organisations combine and analyse data without exposing the underlying records. This guide explains what they are, when they help, and how they relate to external data sourcing.

Available across the EU. DataSupplier sources and delivers this data in all 27 European Union countries — including Germany, France, Spain, Italy, the Netherlands and Poland — and across the EEA, in the format and cadence you need.

What a data clean room is

A data clean room is a controlled environment where parties can combine and analyse data for an agreed purpose without either side seeing the other raw records. Outputs are limited to aggregated or approved results.

Why they matter

They enable collaboration, measurement, enrichment, matching, that would otherwise be blocked by privacy and competitive concerns. As identifier-based approaches fade, clean rooms have become a key privacy-preserving alternative.

The techniques involved

Clean rooms combine access controls, aggregation, and privacy-enhancing technologies such as differential privacy and secure computation to limit what can be learned about individuals.

Common use cases

Marketing measurement and audience overlap, data enrichment and matching, cross-organisation analytics, and research collaboration.

Limits and considerations

Clean rooms reduce but do not eliminate risk; output controls and query limits matter, and the GDPR still applies to the inputs. Governance of allowed queries is central.

In a managed model

A managed partner can prepare and deliver data into clean-room environments, with the anonymisation and governance that make collaboration lawful.

How clean rooms actually work

A clean room is a controlled environment where two or more parties bring data together for an agreed purpose, but neither can see the other’s raw records and only approved, usually aggregated, outputs leave. Under the hood they combine strict access controls, query restrictions and privacy-enhancing techniques such as aggregation thresholds and, increasingly, differential privacy. The governance of which queries are allowed is as important as the technology, because clever queries can otherwise re-identify individuals.

Common use cases and limits

Clean rooms shine for marketing measurement and audience overlap, privacy-preserving enrichment and matching, and cross-organisation analytics that would otherwise be blocked. But they reduce rather than eliminate risk: output controls, query limits and minimum aggregation matter, and the GDPR still applies to the inputs. They are a tool for specific collaborations, not a blanket licence to combine personal data freely.

Sources & further reading

• European Data Protection Board: privacy-enhancing technologies.
• ENISA: privacy-enhancing technologies reports.
• EUR-Lex: Regulation (EU) 2016/679 (GDPR).
• Industry references on data clean rooms.