The complete guide to data clean rooms

In a privacy-conscious world, the ability to collaborate on data without compromising confidentiality is becoming essential. As third-party cookies decay and regulations like GDPR and CCPA reshape what’s possible, organizations are rethinking how they share and use sensitive information.

A data clean room offers one solution. It’s a secure environment where multiple parties can analyze combined datasets, like first-party customer data and partner-supplied information, without exposing the raw data itself. Whether it’s optimizing ad performance across platforms or conducting collaborative medical research, clean rooms make it possible to unlock shared insights while keeping data protected.

This article explores what data clean rooms are, how they work, who uses them, and how to evaluate the right solution for your organization. Along the way, we’ll look at common applications and where clean rooms fit in a modern data strategy.

Key Takeaways

• A data clean room is a secure space where multiple parties can work with combined data, without exposing the underlying information.
• These environments enforce strict controls on who can access what and how data can be used.
• Organizations use clean rooms for ad optimization, joint research, and cross-company analytics — all without compromising privacy.
• Technologies like SMPC, TEEs, and encryption-in-use form the foundation of privacy-preserving data collaboration.
• The right clean room depends on your technical needs, compliance goals, and integration environment.
• Clean rooms make it possible to analyze sensitive data collaboratively, without giving up control or regulatory compliance.

What is a data clean room?

A data clean room is a secure, neutral environment where multiple organizations can analyze combined datasets without sharing the raw data. Privacy protections are built into the infrastructure, making it possible to generate shared insights while keeping sensitive data fully protected.

How does a data clean room work?

A data clean room works by creating a secure environment where multiple parties can collaborate on data without ever exchanging the data itself. Instead of sharing data or giving direct access, each party uploads their data into a controlled environment — often hosted by a neutral provider — where strict privacy rules are enforced from the outset.

The goal is to create a shared space for analysis without requiring any party to compromise on privacy, compliance, or data ownership.

Many clean room providers operate as neutral intermediaries, offering secure environments where data never leaves the control of its owner.

For example, Decentriq enables two or more organisations to collaborate on sensitive data using privacy-enhancing technologies — without ever exposing raw information. Data can’t be accessed by us as the host of the platform or by the cloud provider on which the platform is hosted.

Here’s what that process typically looks like:

1. Data ingestion

Each party uploads their own data into the clean room environment. This may include first-party customer data, transactional data, or behavioral insights. This data remains logically separated and fully controlled by the data owner.

2. Privacy enforcement 

Privacy-enhancing technologies (PETs) such as encryption-in-use, secure multi-party computation (SMPC), or trusted execution environments (TEEs) ensure that no party can see another’s raw data. Personally identifiable information (PII) is either anonymized or protected through strict access policies.

Usage rules are defined in advance. These rules determine what types of analysis are allowed, what queries can be run, and what outputs are permitted. Some clean rooms also support differential policies by partner or dataset.

3. Analysis and computation

Instead of exposing raw data, parties run pre-approved queries or analysis pipelines inside the data clean room environment. These might involve aggregating customer behavior across platforms, measuring ad exposure, or modeling outcomes across combined datasets. All analysis happens behind the scenes, using anonymized data or techniques that prevent individual-level disclosure.

4. Aggregated output

To reduce re-identification risk, only aggregated output is allowed to leave the clean room, meaning output that meets pre-defined thresholds, such as minimum audience sizes or data suppression rules. No row-level data or PII can leave the data clean room.

This combination of access control, data separation, and secure computation enables organizations to generate meaningful insights from joint data collaboration without compromising on privacy, ownership, or compliance.

Clean rooms are especially valuable when working with sensitive data, first-party data partnerships, or multiple datasets across companies or departments. By design, they help organizations share insights, not raw data.
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The privacy-enhancing technologies behind clean rooms

To ensure that sensitive data remains protected throughout the process, clean rooms can rely on a combination of PETs. These include:

Secure multiparty computation (SMPC)

SMPC allows multiple parties to compute joint results without revealing their individual inputs. Each party contributes encrypted values, and the clean room returns only the combined outcome.

Trusted execution environments (TEEs)

TEEs are secure hardware enclaves that isolate computations from the rest of the system. Even the infrastructure provider cannot view or tamper with the data during analysis.

Differential privacy

Differential privacy introduces statistical noise into results, making it mathematically difficult to trace any output back to a single individual. It’s especially useful for large-scale analysis where personal identifiers must be protected.

Together, these technologies help enforce strict separation between data owners while enabling meaningful, privacy-preserving data collaboration.

Who uses data clean rooms — and why?

Data clean rooms are used by a range of professionals who need to work with sensitive or distributed data, without compromising compliance, security, or trust. While the use cases vary, the underlying goal is the same: to extract value from data while protecting it.

Here’s how different teams typically use a data clean room solution:

Marketing and advertising teams

• Collaborate with publishers or platforms to measure campaign effectiveness
• Match user-level data across first-party datasets and media partners
• Unlock audience insights without exposing personal identifiers or customer-level data

Data scientists and analytics teams

• Combine datasets from multiple parties for modeling and analysis
• Work with anonymized data in a secure environment that supports granular queries
• Run advanced analytics on shared data while preserving data ownership

Privacy, compliance, and legal teams

• Ensure data collaboration efforts align with GDPR, CCPA, and internal governance policies
• Validate that no party has access to unpermitted or identifiable data
• Establish clear audit trails and enforce usage controls for all participants

Product and data platform leaders

• Build clean room functionality into customer-facing products
• Evaluate emerging technologies like privacy-enhancing technologies (PETs) and differential privacy
• Enable data sharing features without introducing privacy risk

In each of these roles, the clean room acts as an enabler, not just of data protection, but of responsible innovation. By design, clean rooms support secure data collaboration across teams, companies, or ecosystems, without needing to compromise on control or compliance.

Where are data clean rooms used?

From marketing to healthcare, data clean room solutions are gaining traction across industries that rely on sensitive, high-value data. 

What unites these sectors is the need to collaborate without compromising privacy, intellectual property (IP), or regulatory compliance. Below, we explore two of the most established applications — advertising and healthcare — and how organizations leverage data clean rooms in practice.

Advertising and marketing

In the wake of third-party cookie deprecation announcements and evolving consumer privacy regulations, marketers are rethinking how they target, measure, and optimize campaigns. Data clean rooms have emerged as a critical bridge between privacy and performance.

Key use cases include:

• Audience insights
  Brands and publishers can match user-level data without exposing PII, enabling privacy-preserving audience segmentation.
  
  
• Measurement and attribution
  Clean rooms allow marketers to combine exposure data with conversion events across platforms, helping them measure performance with greater precision and control.
  
  
• Prospecting:
  Brands can extend the reach of their campaigns with new audiences within the publisher universe that share similarities with their existing customers.

Real-world example: IKEA and willhaben

IKEA and Austrian media company willhaben ran a cookieless campaign using Decentriq’s data clean room. By securely matching CRM and publisher data, they reduced cost per visit by up to 30%, proving that first-party data activation can outperform cookie-based methods.

Read the full case study.

Learn more about Decentriq’s advertiser data solutions here.

Healthcare and life sciences

In healthcare, data sensitivity is non-negotiable. Yet real-world evidence, patient outcomes research, and multi-party trials all depend on secure data collaboration. Clean rooms offer a way to work with combined data, while keeping patient identities and protected health information (PHI) private.

Key use cases include:

• Cross-institutional research
  Hospitals and research groups can pool data to detect trends and improve treatment outcomes, without transferring underlying data across systems.
  
  
• Clinical trial optimization
  Pharmaceutical companies can partner with clinical sites to monitor efficacy and safety signals using aggregated outputs, without ever accessing raw patient records.
  
  
• Precision medicine
  Data clean rooms help unify genomic, clinical, and demographic data to enable more personalized therapies, while respecting strict data governance rules.

Discover Decentriq’s data clean room solutions for healthcare.

Why organizations choose Decentriq

Decentriq’s data clean room solution is built to meet the complex demands of both highly regulated sectors like healthcare and fast-moving industries like advertising. Whether it’s analyzing ad exposure data or enabling privacy-preserving clinical research, Decentriq provides a secure, interoperable environment that adapts to your specific use case, without compromising on compliance, performance, or control.

Explore more case studies.

How to choose the right data clean room provider

Choosing a data clean room provider is a strategic decision. The right solution should match your data governance standards, support your use cases, and integrate smoothly into your existing infrastructure. 

Below is a structured guide to help you compare options and make an informed decision.

Privacy and regulatory compliance

At its core, a data clean room is only as strong as its privacy protections. Look for providers that support privacy-enhancing technologies (PETs) such as confidential computing, trusted execution environments (TEEs), and differential privacy. These safeguards ensure that data remains protected, not just in transit or at rest, but during analysis.

You’ll also want assurances that the provider meets relevant data protection regulations such as GDPR, as well as sector-specific rules like HIPAA or EMA guidelines.

Integration and interoperability

A data clean room should work with the tools and data pipelines you already use, not force you to rebuild them. Prioritize solutions that offer seamless integrations with cloud platforms (e.g., Azure, AWS, GCP), identity solutions, and data warehousing tools.

If you're collaborating with external partners, ask whether the clean room supports interoperability across ecosystems and whether it provides flexible onboarding flows.

Output control and analysis capabilities

Different clean room providers offer different levels of control over what can be extracted. If you need aggregated output only, most platforms will suffice. But if your use case requires more granular insights while still preserving privacy, look for a provider that supports customizable output policies and has built-in guardrails to prevent data leakage.

Also, consider whether you need no-code access or if your team will benefit from advanced query support and analyst-friendly interfaces.

Pricing and cost model

Some data clean room providers charge based on usage volume, and others charge based on the number of partners or projects. Make sure you understand the pricing model upfront — especially if you're expecting to scale.

Watch for hidden costs, such as charges for compute, integration work, or adding new collaborators. Transparent, predictable pricing helps avoid friction later.

What are the alternatives to data clean rooms?

Data clean rooms are powerful, but they’re not the only privacy-preserving data collaboration option. Depending on your industry, technical resources, and regulatory environment, other solutions may offer partial alternatives, though often with trade-offs.

Below are some of the most common alternatives and how they compare to clean rooms:

Unlike these approaches, independent data clean room environments are built from the ground up for privacy-preserving data collaboration. They combine granular access control, privacy-enhancing technologies, and secure infrastructure to allow multiple parties to extract value from combined data, without sharing the underlying data itself.

In sectors where compliance, trust, and data security are paramount, a purpose-built data clean room solution often strikes the best balance between control, collaboration, and speed to insight.

Some organisations are also looking to extend the value of their existing data platforms — like DMPs or CDPs — by layering data clean rooms on top to enable privacy-preserving collaboration across teams and partners.

The future of data collaboration and clean rooms

As data ecosystems grow more interconnected — and regulatory scrutiny intensifies — data clean rooms are evolving from a privacy workaround into a foundational layer for secure, cross-party collaboration.

We’re already seeing the next generation of clean room data solutions move beyond static batch analysis. Interoperability is becoming a key differentiator: organizations want to collaborate across multiple clean rooms and vendors without sacrificing control or compliance. Emerging standards like IAB’s Data Clean Room Standards aim to make this future a reality.

Real-time use cases are also gaining traction. In advertising, this means dynamic campaign measurement across platforms. In healthcare, it could support near-instant analysis of combined clinical and genomic datasets, all within a privacy-preserving framework.

AI will further raise the stakes. As models increasingly rely on distributed, sensitive datasets, clean rooms will need to integrate with privacy-enhancing technologies (PETs) that support AI training on encrypted or anonymized data, without exposing source data.

At the same time, new regulations like the EU Digital Markets Act (DMA) and Data Governance Act (DGA) are pushing for greater transparency, fairness, and control over how data is shared. Data clean rooms are uniquely positioned to meet these demands while maintaining business and research agility.

Decentriq is already enabling this shift. Our platform is designed to scale with evolving compliance needs, support federated collaboration, and integrate proven PETs — helping organizations unlock value from sensitive data while staying future-proof.
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“Clean rooms are no longer a niche solution — they’re becoming table stakes for any organization that wants to collaborate on data without compromising privacy. What sets future-ready platforms apart is their ability to combine regulatory-grade security with real-world usability.” — Maximilian Groth, CEO & Co-founder, Decentriq

This shift is being recognized across industries. A Gartner report notes that 60% of large organizations will adopt at least one privacy-enhancing computation technique for processing data in untrusted environments — a trend driven in large part by the rise of clean room technologies.

Together, these perspectives underline what’s at stake: not just better data collaboration, but smarter, safer ecosystems built on trust, control, and compliance.

FAQs about data clean rooms

Are data clean rooms GDPR compliant?

Yes — when implemented correctly, data clean rooms can help organisations meet their obligations under GDPR. They do this by enforcing strict controls on data access, anonymisation, and output. Techniques like differential privacy, encryption-in-use, and secure multiparty computation (SMPC) ensure that no PII is exposed during collaboration. However, compliance also depends on how the clean room is configured and whether usage policies are aligned with legal bases for processing.

How does a clean room differ from a walled garden?

Walled gardens (like Google, Meta, or Amazon) offer closed ecosystems where advertisers can measure campaign performance using the platform’s internal data. While convenient, these environments are controlled by a single provider, which limits transparency, interoperability, and control over data use.

A data clean room provides a neutral environment where multiple parties retain ownership and control of their data. It allows for shared analysis across organisations — not just within a single platform — and enforces privacy-enhancing technologies and output controls to protect sensitive data.

This matters because users actually spend most of their time outside walled gardens. According to Statista, over 66% of U.S. consumer time is spent on the open internet, yet the majority of ad spend still flows into closed platforms. Clean rooms help close this gap by allowing advertisers to activate first-party data and measure performance across the open web, while maintaining full compliance and data protection.

How can I set up a data clean room?

Setting up a data clean room typically involves:

1. Uploading your data into the clean room environment, usually via secure ingestion pipelines or cloud integrations.
   
   
2. Defining usage policies, access controls, and query rules in alignment with your compliance needs.
   
   
3. Collaborating with partners who bring their own data into the environment to run approved analyses.
   
   
4. Extracting results that meet pre-defined privacy thresholds — usually in an aggregated or anonymised form.

Many modern clean room providers offer onboarding support, integrations with major cloud platforms, and flexible configuration options to help you get started.

Data clean rooms enable privacy-preserving data collaboration

Whether you're analysing campaign performance or conducting joint medical research, data clean rooms enable you to unlock insights from combined datasets — without exposing sensitive information or violating compliance.

Decentriq is a data clean room provider that enables organisations to work with sensitive data across partners, without compromising control, compliance, or trust. Powered by a zero-trust architecture and confidential computing, our platform is designed for real-world collaboration in advertising, healthcare, and beyond.

What sets us apart? No one, not even Decentriq as the platform provider, or the cloud where the collaboration takes place, can ever access or view your raw data. This means you retain complete control and confidentiality.

Request a data clean room demo today!