Beyond Data Clean Rooms: When They Work and What to Use Instead

A data clean room is a controlled environment where two or more companies run analysis across their combined data without either side handing over the raw records. Each party makes its data available in a governed space (loaded into a shared environment in the classic model, or queried where it already lives in the newer warehouse-native ones), an operator enforces which queries are allowed, and only aggregated results come back out. It is the standard answer to one question: how do we measure something together without trusting each other with the underlying data?

The catch is that a clean room solves a trust problem by inserting an operator you have to trust instead. For advertising and media measurement that trade-off is usually fine. For enterprise and government data sharing it often is not, and there are architectures that remove the operator entirely. Clean rooms are not bad, they were designed for measurement between advertising partners, and people are now stretching them to cover data sharing problems they were never shaped for. So below: where a clean room fits, how the main platforms compare, and what a data clean room alternative looks like when the model stops fitting it.

Clean rooms grew up in adtech, and that is still where they earn their keep. The flagship use cases are audience overlap, reach and frequency measurement, and last-touch attribution. Snowflake, AWS, and LiveRamp all ship templates for these jobs, which tells you what the category was built for.

A clean room is a good fit when a few things are true at once: you are measuring or matching rather than running operational data flows, the output you need is aggregate rather than row-level records you act on, the other party is a contracted commercial partner rather than a regulator or competitor, and the data is marketing data where a threshold like "suppress any segment under 50 users" is an acceptable cost. The original urgency narrative has softened too: Google confirmed in 2025 that it would not deprecate third-party cookies in Chrome after all, though signal loss from privacy law and walled-garden export limits still drives the category.

We assessed each option against four criteria, weighted by how much they matter for the data sharing problems mintBlue's customers actually face (enterprise and government, not ad campaigns), so the weighting is deliberately tilted away from marketing.

We are not publishing star ratings: the category is too young and configuration-dependent for a clean number to mean anything, and IAB's own research calls clean rooms "far from being turnkey technology." What follows is a comparison table of factual properties, then a section per option mapping its strengths and limits to those criteria.

How it works: Snowflake built its clean room on Samooha, acquired in December 2023, and ships it as a Native App. Each party's data stays in its own Snowflake account, shared as "a live view of the source data, not a snapshot." Queries run against approved JinjaSQL templates, and the shipped templates are the adtech classics: audience overlap, reach and frequency, last-touch attribution.

Strengths: if your data is already in Snowflake, nothing leaves the platform, and cross-cloud collaboration works across AWS, Azure, and GCP.

Honest limits: every participant needs a Snowflake account, and providers need Enterprise Edition or higher. Cross-region collaboration replicates the data, so data does move and you pay egress. Anything not already in Snowflake has to be loaded in first, and compute is open-ended credit burn, hard to predict per collaboration.

Verdict: best when your data already lives in Snowflake and your partners will too.

How it works: AWS Clean Rooms is a collaboration workspace between AWS accounts that reads data from its original location (S3, Glue, Athena, and now Snowflake as a source), so collaboration does not require moving data out of AWS. The controls are unusually rich: analysis rules constrain the SQL and outputs, Cryptographic Computing for Clean Rooms (C3R) encrypts selected columns client-side and supports joins on that protected data without decrypting it (column-level, not enclave-style encryption of all processing), and analysis logs give an audit record. Differential privacy is a managed add-on, and Clean Rooms ML does lookalike modelling without sharing data.

Strengths: data stays in place within AWS, the privacy controls are the broadest of the hosted options, and AWS is the rare clean room with transparent public pricing (Spark SQL at $2.00 per CRPU-hour, differential privacy adding another $2.00, with separate metered rates for ML and entity resolution).

Honest limits: collaborations run between AWS accounts, so everyone has to be in AWS Clean Rooms. Regional availability is limited (11 regions), costs are usage-metered and climb quickly, and setup is IAM-and-Glue heavy engineering work, not a turnkey tool for a marketer.

Verdict: strongest if you already live in AWS, want transparent pricing, and have engineering capacity to set it up.

How it works: Ads Data Hub is effectively two BigQuery projects connected by an API. Google keeps its ad-event data in a Google-owned project, you upload first-party data into your own BigQuery project, and SQL joins happen inside ADH's controlled environment. Most queries must aggregate over at least 50 users, segments below the threshold are suppressed, and there is no user-level export.

Strengths: since Google stopped user-level exports, ADH is the only way to query Google ad-event data joined with your own first-party data at the user level, even though every result it returns is aggregated. That privacy-checked join is why people use it.

Honest limits: this is a walled-garden clean room. ADH covers Google's media only, no Meta, Amazon, LinkedIn, or TV data, and Google decides how performance is measured. Aggregate-only outputs, query frequency caps, and a batch orientation make it a narrow measurement tool rather than a general collaboration platform. There is no public price list.

Worth a separate mention is PAIR (Publisher Advertiser Identity Reconciliation), a protocol Google's ads team built and handed to IAB Tech Lab (v1.1 released July 2025). PAIR uses commutative encryption so an advertiser and a publisher can match first-party data without either side seeing the other's cleartext, and it was designed to interoperate between clean rooms owned by different parties. The industry needed a cryptographic protocol on top of the clean rooms to make them talk to each other, a sign that the protocol, not the room, was the thing that did the privacy work.

Verdict: the right tool if you need Google media measurement, and nothing beyond that.

How it works: LiveRamp acquired Habu for around $200M in January 2024 and sells the platform as LiveRamp Clean Room, orchestrating queries against data in each party's own warehouse. The differentiator is identity. Customers connect a dataset of identifiers, LiveRamp resolves them against its proprietary graph into RampIDs (a persistent pseudonymous identifier), and cross-party joins happen on the RampID. So while the analytical data can stay where it is, the identifiers flow through LiveRamp, the trusted intermediary that performs the match.

Strengths: the largest identity graph and activation network in the category, with row-level activation to a 1,000-plus partner network, which is why retail media networks lean on it.

Honest limits: the model rests on trusting LiveRamp as the central identity intermediary. Matching happens on LiveRamp's RampID rails, not through cryptography you can verify, and identifier data has to pass through LiveRamp to be resolved. Pricing is opaque and enterprise-sized, identity resolution quality is US-centric per LiveRamp's own documentation, and the "zero-copy" claim needs a caveat: the analytical data can stay put, but identifiers still flow through LiveRamp's resolution and the RampID mapping tables have to be built and maintained.

Verdict: best when identity resolution and activation reach are the point, and a central intermediary is acceptable.

How it works: WPP acquired InfoSum in April 2025 and folded it into GroupM (now WPP Media). Each data owner's hashed and encrypted data sits in a standalone private-cloud "Bunker" that only that owner controls, and raw data never leaves it. For cross-party matching, an anonymous mathematical model carrying no PII moves between Bunkers. InfoSum brands this "non-movement of data."

Strengths: no central pooling of raw data, datasets staying in owner-controlled instances. It is the most architecturally decentralised of the adtech clean rooms.

Honest limits: the acquisition raises a neutrality question that the trade press flagged directly, since InfoSum now sits inside a media-buying agency and competing holding companies have to weigh whether to put their data infrastructure with a rival (a concern that mainly bites for agency and media buyers, less so outside that world). Data still has to be onboarded into an InfoSum-managed Bunker instance, so it is not literally your own infrastructure, and it is oriented toward aggregate insights rather than bespoke analytics.

Verdict: the most decentralised adtech clean room, for aggregate audience work where WPP ownership is not a conflict.

How it works: Decentriq, based in Zürich, runs its clean rooms inside trusted execution environments (secure enclaves), so data is encrypted at rest, in transit, and in use. The vendor's claim is that no one, not even Decentriq or the cloud provider, can see what goes in or out, verifiable through remote attestation.

Strengths: the strongest technical trust model among the hosted clean rooms, hardware-enforced encryption-in-use plus attestation rather than policy-and-contract access control. It also has serious non-adtech references (a Roche healthcare partnership and a Swiss Re insurance reference), which is rare in this category.

Honest limits: data still moves. Encrypted datasets get uploaded into Decentriq's enclave environment, so it is "can't-read-it" centralisation rather than no centralisation. The trust anchor becomes the CPU vendor's TEE security (Intel SGX has a documented history of academic side-channel research), and it is a smaller, venture-backed vendor next to the hyperscalers.

Verdict: the most defensible hosted option for sensitive aggregate analytics (healthcare, insurance) where data can move into an enclave.

How it works: Karlsgate Identity Exchange is a decentralised identity-matching network rather than a hosted analytics environment. Matching is orchestrated across three points (your network, your partner's, and a temporary cloud facilitator node that is destroyed after each trade), and only single-use cryptoidentities ever leave your environment.

Strengths: no data warehouse, no persistent third-party copy, and Karlsgate itself never sees raw identifiers. Both parties keep full custody, and row-level enriched records can be exchanged directly once a match is made.

Honest limits: narrower than a clean room. It is a matching and exchange protocol, not an environment for joint analytics; analysis happens after the exchange, in each party's own stack. It is a smaller vendor with a proprietary, trademarked protocol rather than an open standard.

Verdict: a genuine clean-room alternative when the job is matching and enrichment between partners, not joint analytics.

mintBlue is not a clean room, and for a chunk of what clean rooms do it is the wrong tool. It does not do ad-campaign measurement, run a marketing identity graph, or match hashed emails between an advertiser and a publisher. If your job is audience overlap, use one of the tools above.

What mintBlue does is the layer underneath: decentralised data sharing infrastructure where data stays at its source and moves peer-to-peer between organisations, with every exchange notarised in an audit trail all parties can verify. The mental model is a postal service. Each party seals its own envelope, hands it over, and mintBlue delivers it without ever opening it. There is no central operator holding the keys. A clean room asks you to trust the operator; this approach asks you to trust the protocol.

The mechanism is cryptographic, not contractual. Each party signs its own events with its own keys, machines verify those signatures before processing, and every exchange is anchored in a tamper-evident audit trail tied to legal digital identity (eIDAS/SSI). Because there is no shared warehouse, the architecture supports row-level, operational data sharing rather than aggregate measurement, and it has handled enterprise-scale volumes (mintBlue reported a record 50 million transactions anchored in a single day in 2024). The same layer carries identity and value transfer, so released data can trigger an actual settlement, and you can program rules cryptographically so data is released automatically when a condition is met (an invoice is paid, multiple authorities flag the same company number). It is built for data sovereignty and cross-organisational data governance in regulated settings, where an operator who can technically see everything is a non-starter, and where the output is anchored in that verifiable audit trail and can be made legally binding rather than left as a number on a dashboard.

Honest limits: this is heavier infrastructure than a marketing clean room, and the regulated, government-facing work where it shines (collaborations with tax authorities and fraud-fighting agencies) is largely in proof-of-concept and pilot rather than mass production. It does not fit the advertising measurement world or a quick aggregate overlap count between commercial partners. For where mintBlue does fit in practice, see secure AI and event streams.

Step back from the products and a pattern shows up across the category.

The privacy branding oversells the reality. Most large clean room deployments still require copying or loading data into someone's environment (into Snowflake, into AWS configured tables, through LiveRamp's resolution, into InfoSum Bunkers, or encrypted into Decentriq enclaves), so architectures where data genuinely never leaves your infrastructure are the exception. Query restrictions then cap the analytical value: privacy thresholds and template-only querying rule out user-level attribution, CRM enrichment, and operational data sharing.

The deeper issue is that the operator is a single point of trust. The strength of that trust varies (warehouse-native governance, analysis rules and logging, remote attestation in an enclave are not equal), but in every hosted model someone runs the environment and enforces the rules. The trust never disappeared when the data sharing got harder, it simply moved to the platform. Interoperability shows the same pattern: clean rooms do not natively talk to each other, and the industry's fix was a cryptographic protocol (PAIR) layered on top, a hint that the protocol was the thing you needed in the first place.

The cost data underlines all of this. IAB's State of Data research found 62% of clean room users spent at least $200,000 in a year, yet fewer than a third use the advanced capabilities. The alternative paradigms the industry is now reaching for (cryptographic matching, private set intersection, federated analytics where the computation goes to the data) point at architectures where the operator is removed rather than trusted. Several rest on the same family of cryptographic primitives, including the Oblivious Pseudorandom Function (OPRF, standardised as RFC 9497), which lets two parties match on shared values without either side, or any middleman, seeing the other's inputs. That is the same primitive mintBlue uses in its privacy-preserving data-sharing work for fraud-fighting authorities, bringing signals together without disclosing the underlying records. For more, see data interoperability.

The right choice follows from the situation, not from which vendor markets hardest. If you are measuring campaigns and matching audiences with commercial partners, a clean room is the correct category, and you pick by ecosystem (the verdicts above map each to its sweet spot). If you need the strongest privacy guarantee for sensitive but still aggregate analysis and can accept data moving into an enclave, Decentriq is the most defensible hosted option.

If your problem is operational data sharing across organisational boundaries, between regulators and the market, across a supply chain, or between parties who do not trust a shared operator (competitors included), and you need row-level, real-time exchanges anchored in a verifiable audit trail, that is where the clean room model stops fitting and a decentralised, peer-to-peer architecture like mintBlue's belongs. Measuring between advertising partners points to a clean room. Sharing operational data between organisations that cannot afford to trust an operator points to removing the operator instead.

What is a data clean room?
A data clean room is a controlled environment where two or more organisations analyse their combined data without handing over the raw records. Each party's data stays in its own space, an operator enforces which queries can run, and only aggregated results come back out. It is most common in advertising for audience overlap, reach, and attribution.

How does a data clean room work?
A clean room combines three controls: data placement (each party's data sits in its own account, queried where it lives rather than pooled), query restriction (only approved, usually templated queries run, with privacy checks like minimum-audience thresholds), and output control (results are aggregated and anything that could re-identify an individual is blocked). The operator enforces these rules, so protection rests on trusting that operator.

What is the best alternative to a data clean room?
It depends on the job. For privacy-preserving identity matching, cryptographic protocols like PAIR or networks like Karlsgate remove the shared warehouse. For operational, cross-organisational data sharing where you cannot trust a central operator, decentralised infrastructure that keeps data at source and exchanges it peer-to-peer with an audit trail is a better fit than any clean room.

Are data clean rooms expensive?
They can be. IAB research found 62% of users spent at least $200,000 in a year and 23% spent over $500,000, with implementations taking up to two years. Fewer than a third use the advanced features, so a lot of that spend supports basic overlap counts.

Is a data clean room the same as confidential computing?
No. Confidential computing (used by Decentriq) runs analysis inside hardware-enforced secure enclaves so the operator cannot read the data even while processing it. Many clean rooms rely on policy and contract instead. Confidential computing can power a clean room, but most do not use it, and even with it the encrypted data still moves into the enclave.