The demand for more data remains unbroken—in marketing, finance, as well as in research. At the same time, businesses and institutions face regulatory pressure to protect personal data in compliance with the GDPR. In this context, Data Clean Rooms (DCRs) are gaining momentum as a technical and legal infrastructure for secure collaboration between data-processing entities.
What is a Data Clean Room from a Privacy Perspective?
A Data Clean Room allows multiple parties to combine their datasets in a shielded analytics environment—without raw data leaving the parties’ own infrastructure or becoming visible to other participants. Data is often shared in hashed, aggregated, or pseudonymized form. The economic appeal is clear: businesses can monetize their data, gain insights even through cooperationwith competitors, or optimise their business processes—without disclosing trade secrets or risking re-identification.
Data Protection as an Enabler – Not a Showstopper
From a legal perspective, the key question is the following: does the operator of the Data Clean Room have access to any personal data? If the system is designed in such a way that a re-identification of the data subjects behind the data sets is technically impossible for the DCR operator, the transmitted data could be considered anonymised. In this case, the GDPR would not fully apply—particularly regarding transparency obligations, legal basis requirements, or retention periods.
This very issue is currently being reviewed by the European Court of Justice (ECJ): in the so-called SRB case, the court is assessing the definition of “personal data” in relation to pseudonymised information. Should the ECJ endorse the so-called relative identifiability test—that is, whether a specific data recipient can identify a data subject—this would have far-reaching implications. It would pave the way for simplified and GDPR-compliant application of many Data Clean Room models.
Currently, six use cases are emerging where Data Clean Rooms can provide added value:
- Campaign and Attribution Measurement
CRM-derived hash IDs can be matched with publisher data—without either party accessing raw data. Particularly relevant as third-party cookies are phased out. - Second-party data enrichment
Two businesses combine customer lists to analyze shared audiences—without exposing master data. A win for both privacy and antitrust compliance. - Product and market research
Loyalty data, panel responses, and social listening data are analysed under differential privacy constraints—ensuring that individual statements remain statistically untraceable. - Fraud and risk scoring
Banks can exchange suspicious transaction patterns; the DCR calculates risk scores. This is especially useful in highly regulated industries, supported by Secure Multi-Party Computation (SMPC). - Group-wide 360° analytics
Subsidiaries share pseudonymized IDs; the holding company receives consolidated KPIs. From a privacy perspective, this is often permissible on the basis of legitimate interests (art. 6(1)(f) GDPR). - Federated learning for AI modell
An AI model can be trained where the data resides. Only gradients are transmitted to a central location—an innovative approach to privacy-friendly AI development
Key Governance requirements for compliant technology use
For a Data Clean Room to be effective both technically and in a legal sense, clear role definitions and binding contractual frameworks are essential. The European Data Protection Board’s Guidelines 07/2020 offer crucial guidance on distinguishing between joint controllership and processor relationships for this purpose.
Equally important are robust technical safeguards, such as:
- TLS 1.3 and AES-256 encryption for secure transmission and storage,
- purpose-specific access control models,
- query throttling,
- and immutable audit logs to track queries, retention periods, and data subject rights.
Conclusion and outlook
Data Clean Rooms offer a new way to create data-driven value while protecting user privacy. They are becoming a cornerstone for GDPR-compliant collaboration—particularly when technical and organisational safeguards effectively prevent third-party re-identification.
If the ECJ confirms the principle of relative identifiability, Data Clean Rooms may soon evolve from a niche concept to the standard model for collaborative data analytics.
We are closely monitoring these legal developments—and are happy to support you in building, evaluating, and implementing privacy-compliant Clean Room solutions.
(Dr. Lukas Mezger, UNVERZAGT Rechtsanwälte)