Data Clean Room

Two companies each have a long list of their customers. They would like to know how many people appear on both lists. Simply exchanging the lists would be legally risky and commercially unwise. A data clean room solves this problem: both sides upload their data into a shielded computing environment that no one has direct access to. There, the data is combined and analyzed. Only the result of the analysis reaches the outside world, such as a number or a statistic, never the individual data records themselves.

Why advertising without cookies needs new solutions

For years, online advertising worked through cookies, small identification files stored in the browser. With them, companies could track individual users across the internet. Data protection laws and the browser makers themselves have restricted this significantly. As a result, the advertising industry suddenly lost its most important tool. Data clean rooms are one of the answers to this.

An example: a sporting goods manufacturer runs ads on a large platform. It wants to know whether the people who saw the ad actually went on to buy shoes. The platform knows the viewers, the manufacturer knows the buyers. Neither is allowed to give the other its list of names. In the clean room, it is nevertheless possible to calculate how large the overlap is.

On top of this comes legal pressure. The European General Data Protection Regulation prohibits simply passing on personal data. Companies therefore need procedures that technically prevent what would be prohibited by contract anyway. A clean room is also proof to regulators that a company has made an effort.

Matching without insight: the technology behind it

The first step is usually encrypting the identifiers. An email address is turned into a long string of characters using a fixed computational rule. This transformation cannot be reversed. Both sides use the same rule. Identical addresses therefore produce identical strings, and matching works without a readable address ever appearing anywhere.

The second building block is strict rules for queries. Not every question is allowed. A typical rule is a minimum size: results are only output if they concern at least fifty or a hundred people. Otherwise, by cleverly combining many small queries, one could still infer information about individual people. Some systems additionally add tiny random deviations to the results to make this kind of inference even harder.

A common misconception is that the clean room is a particularly secure data vault. That’s not quite right. A vault protects data from outsiders. A clean room protects the two participants from each other, even though they are voluntarily working together. It is usually operated by a neutral third party or directly by a cloud provider, that is, a company that rents out computing power over the internet.

Who operates and uses clean rooms

The best-known offerings come from the large platforms. Google, Amazon, and Meta operate their own clean rooms for advertising clients. Database providers such as Snowflake or Databricks also sell such environments. In Germany, retail chains and publishers are working on joint solutions in order to hold their ground against the American platforms.

The field of application extends beyond advertising. Hospitals can pool treatment data for studies without disclosing patient records. Banks can match patterns of fraud cases without sharing account data. Wherever several parties want to compute jointly but are not allowed to, the principle becomes interesting.

In business news, the term usually comes up when two corporations announce a data partnership. Critics point out that clean rooms tend to reinforce the market power of the large platforms. After all, whoever holds the most user data sets the rules of the room. Data protection advocates also emphasize that technology alone does not replace consent: users must still agree to the use of their data.