Data clean rooms

The best data clean room solution depends on which data you need to collaborate on, where that data lives, and how much control you need over what partners can learn or extract.

A data clean room lets two or more organizations analyze overlapping datasets without routinely sharing their underlying raw data. Common use cases include joint customer analytics, advertising measurement, retail media, financial services, and secure AI or machine-learning collaboration.

For a secure enterprise deployment, I would evaluate solutions against five criteria:

• Data isolation: Keep source data in your own environment wherever practical.
• Privacy enforcement: Apply minimum group-size thresholds, query restrictions, aggregation, and, where appropriate, differential privacy.
• Access control: Enforce least privilege, partner-specific permissions, and identity federation.
• Auditability: Record queries, exports, policy changes, and administrative activity.
• Deployment flexibility: Support your cloud, data warehouse, partner technology stack, and regulatory requirements.

One important distinction: a clean room does not automatically guarantee that sensitive information cannot be inferred from results. Query controls, output inspection, and protections against repeated or overlapping queries matter just as much as encryption.

My shortlist of the best data clean room solutions

These are the platforms I would put on an enterprise shortlist, based on their architecture, collaboration flexibility, and privacy controls—not a universal ranking.

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1. AWS Clean Rooms

(Best for AWS-centric enterprises)

A strong starting point if your data is in AWS and you want managed, policy-controlled SQL analytics, machine learning, and collaboration without routinely moving raw datasets. It supports query restrictions, output controls, audit logging, differential privacy, and cryptographic computing options.aws.amazon.com+1

Watch out for: collaboration limits and the operational costs of running queries across different data platforms.

Official product site

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2. Snowflake Data Clean Rooms

(Best for warehouse-native collaboration)

My first choice to evaluate if your organization already uses Snowflake. It offers governed collaboration for technical analysts and business users, including SQL-based analysis and cross-cloud collaboration. It also targets use cases beyond marketing, including financial services and healthcare.www.snowflake.com

Watch out for: fit with partners who use different platforms, plus the cost and complexity of the surrounding Snowflake environment.

Official product site

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3. Databricks Clean Rooms

(Best for collaborative AI and advanced analytics)

A strong option when partners need to run sophisticated analyses or machine-learning workflows, not just predefined SQL queries. It supports multiple clouds and data platforms, with code approval and governed collaboration.www.databricks.com+1

Watch out for: the technical expertise and governance process needed to review and approve collaborative code.

Official product site

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4. InfoSum

(Best to evaluate for decentralized collaboration)

Worth considering when multiple partners want to match and analyze customer datasets while keeping data in their respective environments. Its decentralized architecture is particularly relevant to advertising, media, and multi-party audience analytics.www.infosum.com

Watch out for: validating support for your specific non-marketing workflows, integrations, and contractual privacy requirements.

Official product site

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5. BigQuery data clean rooms

A good fit for organizations already using Google Cloud and BigQuery. Data-sharing controls, aggregation rules, export restrictions, and predefined query templates help govern sensitive cross-organization analytics.docs.cloud.google.com

Watch out for: Google Cloud and BigQuery dependencies, and the need to design query templates carefully to limit data leakage.

Official documentation

How I'd choose

These are starting recommendations, not proof that one vendor is inherently more secure than another. The winner depends on your threat model, partner environment, and how much control you need over computations and outputs.

What makes a clean room genuinely secure?

Before choosing a vendor, I would require a proof of concept that tests these controls with realistic partner data.

Do not rely solely on a vendor's claim that its product is privacy-preserving. For example, Google explicitly cautions that analysis rules alone might not prevent sophisticated data-extraction queries and recommends query templates for tighter execution control.docs.cloud.google.com

My recommendation

I would begin with AWS Clean Rooms, Snowflake, and Databricks, then include InfoSum if keeping data decentralized is a priority. Run the same threat-model and leakage tests against the finalists before committing.

To narrow this down to the best fit for your situation, I have one key question:

1. What kind of collaboration are you building?

Enterprise data sharing and analytics

Marketing, advertising, and customer matching

Healthcare, financial, or other regulated data

Joint AI or machine-learning development

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2. Where does your data live today?

AWS / Amazon S3 / Redshift

Snowflake

Databricks

Google Cloud / BigQuery

Multiple clouds or platforms

Other / not decided yet

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3. How sensitive is the data?

Business data with limited personal information

Customer PII or commercially sensitive data

Highly regulated data or strict no-exposure requirements

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4. How many partners will collaborate?

1–3 external partners

4–10 partners

A large or growing partner network

[Find my best-fit solution]

Top secure data clean room platforms for partner collaboration

A data clean room lets two or more organizations analyze overlapping or combined datasets while restricting access to the underlying sensitive records. It can help partners collaborate on customer insights, advertising measurement, financial analysis, healthcare research, and other data-driven projects without freely exchanging raw data.

For an enterprise evaluating platforms in 2026, I'd shortlist AWS Clean Rooms, Snowflake Data Clean Rooms, LiveRamp Clean Room, InfoSum, and Decentriq. The best choice depends on where your data lives, how much technical flexibility you need, and whether you prioritize marketing collaboration or broader enterprise use cases.aws.amazon.com+3

1. The leading options at a glance

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1. AWS Clean Rooms

(Best for AWS-centric teams)

Lets partners analyze their data without moving or revealing their underlying datasets. Offers SQL and machine-learning workflows, configurable analysis restrictions, differential privacy, and integration with AWS data services and Snowflake.aws.amazon.com+5

Best for: Enterprises already using AWS, multi-party analytics, and privacy-sensitive collaboration.

Official website

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2. Snowflake Data Clean Rooms

(Best for existing Snowflake users)

Enables organizations to combine and analyze partner datasets in a governed environment. Data owners can restrict permitted analyses, while collaborators receive approved insights rather than unrestricted access to raw records. Supports a business-friendly interface and developer APIs.aws.amazon.com+8

Best for: Data warehouse teams, enterprise analytics, and organizations that want to build on their existing Snowflake environment.

Official documentation

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3. LiveRamp Clean Room

(Best for marketing data collaboration)

Provides privacy and governance controls including data minimization, role-based access, query transparency, dataset analysis rules, and privacy-enhancing technologies. Particularly relevant for brands, publishers, retailers, and advertising partners.aws.amazon.com+6

Best for: Audience insights, campaign measurement, identity-based data collaboration, and marketing partnerships.

Official documentation

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4. InfoSum

A specialist data collaboration platform worth evaluating when partners need to match or analyze datasets while keeping control of their own data. It is particularly associated with enterprise and marketing-oriented data collaboration.

Best for: Organizations collaborating across multiple data owners, especially where data sharing and privacy boundaries are major concerns.

Official website

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5. Decentriq

A privacy-focused option to investigate for sensitive cross-company data collaboration, particularly where confidential computing and strong technical protections are important evaluation criteria.

Best for: Organizations handling highly sensitive data and seeking stronger isolation between data owners and analysts.

Official website

These are a use-case-driven shortlist, not a universal ranking. The strongest fit depends on your data architecture and required privacy guarantees. For InfoSum and Decentriq in particular, validate current integrations, deployment options, and contractual assurances directly with the vendors before making a decision.

2. How I would choose between them

These recommendations reflect documented platform capabilities, not independent security test results.aws.amazon.com+4

3. What to evaluate before selecting a vendor

A secure clean room is only as strong as its technical controls, configuration, and governance. I would ask every vendor to demonstrate these six capabilities:

Data isolation and encryption

Confirm encryption at rest and in transit, key-management options, and whether the provider or its administrators can access data during processing.

Fine-grained access and query controls

Partners should be restricted to approved datasets, columns, queries, outputs, and purposes.

Output privacy protections

Look for minimum aggregation thresholds, differential privacy where appropriate, and safeguards against re-identification through repeated queries.

Auditing and governance

Verify detailed activity logs, partner-specific permissions, approval workflows, retention controls, and the ability to revoke access.

Integration and interoperability

Check support for your cloud, warehouse, identity-resolution requirements, analytics tools, and partners' existing infrastructure.

Compliance and commercial terms

Request current independent audit reports, applicable certifications, data-processing agreements, data residency details, incident-response commitments, and deletion guarantees.

AWS documents its encryption, IAM, audit logging, and differential privacy controls; Snowflake documents its privacy-preserving analysis model; and InfoSum and Decentriq describe their respective privacy-enhancing approaches. Verify the precise guarantees against your own threat model and contractual requirements.docs.aws.amazon.com+4

4. My recommendation

I would begin with AWS Clean Rooms and Snowflake Data Clean Rooms if you want a general-purpose enterprise analytics solution, then compare InfoSum and Decentriq if keeping data decentralized or protecting data during computation is especially important. If your primary use case is advertising, audience overlap, and campaign measurement, include LiveRamp in the first round.

One important distinction: a clean room does not automatically guarantee compliance or eliminate privacy risk. Configuration, permitted outputs, contracts, and the sensitivity of the data still matter.

5. Let me narrow this down for your organization

A couple of details will make the shortlist much more useful.

What is your primary collaboration use case?

Enterprise analytics across partners

Advertising, marketing, and audience measurement

Financial services, fraud, or risk analytics

Healthcare or other highly sensitive data

AI and machine learning across organizations

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Where does most of your data currently reside?

AWS

Snowflake

Google Cloud / BigQuery

Microsoft Azure

Multiple clouds or mixed infrastructure

On-premises or other

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What is your most important selection criterion?

Maximum privacy and security

Ease of onboarding partners

Integration with our existing stack

Lowest total cost of ownership

Flexible analytics and machine learning

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How many external partners do you expect to collaborate with?

1–4 partners

5–20 partners

More than 20 partners

[Recommend the best-fit platforms]

The top secure data clean room platforms for partner collaboration include Snowflake Data Clean Rooms, LiveRamp Clean Room, AWS Clean Rooms, and Google Cloud BigQuery Clean Rooms .liveramp.com+1

These enterprise-grade solutions offer distinct advantages depending on your existing tech stack, multi-cloud requirements, and specific collaboration goals (such as media measurement, customer attribution, or data monetization).snowflake.com+2

Top Data Clean Room Platforms

• Snowflake Data Clean Rooms : Built on Snowflake's data sharing architecture (enhanced by the acquisition of Samooha), this platform enables cross-cloud collaboration across AWS and Azure without moving raw data. It provides user-friendly, industry-specific templates and relies on native governance and Snowpark for advanced privacy controls.admonsters.com
• LiveRamp Clean Room : Widely recognized as a leader for marketing and media ecosystems, LiveRamp provides a fully interoperable environment. It excels at identity resolution, allowing brands, agencies, and publishers to collaborate and measure campaign performance across diverse partner networks without exposing underlying personally identifiable information (PII).gartner.com+1
• AWS Clean Rooms : Ideal for organizations embedded in the Amazon Web Services ecosystem, this tool allows multiple parties to analyze and collaborate on AWS-native datasets. It features granular, built-in privacy controls—such as query controls, mathematical restrictions, and differential privacy—so that raw data never leaves the owner's account.liveramp.com
• Google Cloud BigQuery Clean Rooms : Tailored for teams already utilizing Google Cloud, BigQuery Clean Rooms leverage Analytics Hub to let organizations securely share data and derive joint insights with external partners while enforcing strict access restrictions and governance rules.liveramp.com

Key Evaluation Criteria

If you can share:

• Your primary data warehouse or cloud provider (e.g., Snowflake, AWS, Google Cloud)
• Your main use case (e.g., marketing attribution, retail-CPG data sharing, or direct data monetization)

I can help narrow down the best platform fit for your organization.