Digital clean room platforms have moved from niche privacy tools to core infrastructure for modern marketing and measurement in 2025. They let brands and publishers collaborate on insights and targeting without exposing raw personal data. This review explains how leading options work, what “privacy-safe” really means in practice, and how to choose the right fit for your stack. Ready to cut through vendor noise?
What Are Digital Clean Rooms and Why They Matter for Privacy-Safe Targeting
A digital clean room is a controlled computing environment where two or more parties (for example, an advertiser and a publisher, or a brand and a data provider) can analyze and activate data together while limiting exposure of underlying user-level information. The goal is practical: improve targeting and measurement while reducing privacy risk and complying with regulatory and platform constraints.
In 2025, several pressures make clean rooms essential rather than optional:
- Regulatory expectations: Enforcement of privacy rules requires minimizing data sharing, tightening access controls, and demonstrating purpose limitation.
- Platform and browser limits: Identifiers are less available, and data access is more restricted in major ecosystems.
- Rising security and governance demands: Boards and security teams increasingly expect auditable controls, not ad hoc file transfers.
Clean rooms generally support two core use cases:
- Measurement: Overlap analysis, reach and frequency, conversion attribution, incrementality testing, and media effectiveness reporting.
- Activation: Creating privacy-protected audiences for targeting (often via aggregated outputs or approved audience exports), and building suppression lists without exposing raw identifiers.
It’s important to define “privacy-safe targeting” realistically. A clean room does not magically make any data use permissible. Instead, it provides technical and procedural safeguards—such as access restrictions, query controls, aggregation thresholds, and audit logs—so that collaboration can occur with reduced re-identification risk and improved compliance posture.
Key Features of Clean Room Technology You Should Compare
Not all platforms marketed as clean rooms enforce the same protections. When evaluating vendors, prioritize mechanisms that measurably reduce risk and support governance. The most decision-critical features typically include:
- Data ingress and identity handling: How data is uploaded (batch, streaming), how identifiers are transformed (hashing, encryption), and whether the platform supports multiple identity strategies. Look for clear documentation on how matches occur and what is stored.
- Privacy controls and query governance: The strongest solutions enforce policy by default, including minimum aggregation thresholds, limits on joining small cohorts, and prevention of “differencing attacks” (where repeated queries reveal individuals). Ask whether they support row-level security, column masking, and approved query templates.
- Output constraints: A privacy-safe clean room typically outputs aggregates, modeled insights, or constrained audience artifacts rather than raw user-level rows. Understand what can be exported, to where, and under what approval workflow.
- Auditability: Look for immutable logs, role-based access controls, and the ability to prove who ran what query, when, and with which data sets. Strong audit trails support internal reviews and vendor risk assessments.
- Interoperability: Practical value depends on integration with cloud data warehouses, CDPs, DSPs, and analytics tools. Consider whether the clean room sits inside your cloud, connects externally, or requires duplicate data storage.
- Collaboration model: Some platforms are “publisher-first” (optimized for media owners), others are “warehouse-native” (optimized for brands with strong data teams). Match the model to your operating reality.
Two follow-up questions usually decide the shortlist:
- Can we run our preferred measurement methods? If you need incrementality or multi-touch experiments, confirm support for holdouts, randomized assignments, and statistically valid reporting.
- Can we actually activate outcomes? Measurement-only clean rooms can be useful, but many teams need closed-loop activation (for example, building a high-value audience and pushing it to approved channels).
Walled Garden Clean Rooms vs. Warehouse-Based Clean Rooms
Clean rooms fall into two broad categories. Understanding the trade-offs helps avoid expensive dead ends.
Walled garden clean rooms are operated by major platforms that control large authenticated audiences and their own advertising inventory. Their advantages include strong first-party identity, native measurement for their ecosystems, and tight integration with their ad products. Limitations often include:
- Scope constraints: Measurement and activation may be limited to that platform’s inventory and rules.
- Less flexibility: You may have fewer options for custom modeling, external joins, or exporting insights for cross-channel use.
- Comparability challenges: Each garden reports differently, making unified measurement harder unless you invest in harmonization.
Warehouse-based clean rooms run within or alongside cloud data platforms. They tend to offer more flexibility for advanced analytics, data science workflows, and cross-channel measurement. Their advantages include:
- Data proximity: If your customer and transaction data already lives in a warehouse, collaboration can happen without excessive data movement.
- Customization: You can often implement bespoke schemas, models, and governance rules.
- Broader partnerships: These solutions can support multiple publishers and data providers through standardized collaboration patterns.
However, warehouse-centric approaches also bring responsibilities:
- You own more governance: The vendor may provide guardrails, but your team still needs policies, approvals, and monitoring.
- Operational complexity: Data engineering, identity strategy, and partner onboarding can be non-trivial.
For many organizations, the most workable approach in 2025 is a hybrid: use walled garden clean rooms for platform-specific measurement and warehouse-based clean rooms for cross-partner analytics and unified reporting. The key is designing a consistent privacy and governance layer so results remain comparable and defensible.
Top Digital Clean Room Platforms for Advertisers and Publishers (2025)
This section focuses on how leading platforms tend to differentiate, using practical evaluation lenses rather than marketing claims. Specific capabilities can vary by region, contract, and configuration, so treat this as a structured review framework you can validate in demos and security reviews.
Google Ads Data Hub (ADH) is a strong choice for advertisers and agencies that need privacy-safe measurement within Google’s advertising ecosystem. ADH is commonly used for reach, frequency, and conversion measurement with strict output controls. It typically suits teams that prioritize in-platform analysis over custom cross-channel exports.
Strengths: mature governance and aggregation controls, deep integration with Google media and measurement workflows.
Watch-outs: scope is primarily within Google’s environment; cross-platform standardization requires additional work.
Amazon Marketing Cloud (AMC) is frequently adopted by brands investing in Amazon Ads. It supports analysis across Amazon signals and campaign performance in a controlled environment, often enabling insights for media optimization and audience creation under Amazon’s rules.
Strengths: robust insights for Amazon-centric strategies, practical for retail media optimization.
Watch-outs: primarily optimized for Amazon inventory and data; ensure your broader measurement plan is not overly dependent on one ecosystem.
Meta’s clean room capabilities (via privacy-enhancing measurement workflows) are relevant for advertisers needing Meta-native analysis, including campaign performance and conversion measurement under Meta’s policies. These solutions usually emphasize privacy controls and aggregated reporting.
Strengths: strong for Meta campaign analytics and reporting compliance within that environment.
Watch-outs: cross-channel comparisons require normalization; activation and export constraints can affect broader targeting plans.
Snowflake Clean Room is often favored by organizations already using Snowflake as a central data platform. It typically supports collaboration via governed data sharing patterns, templates, and controls, enabling brands to work with multiple partners without copying data into separate silos.
Strengths: proximity to enterprise data, scalable collaboration across many partners, strong ecosystem alignment.
Watch-outs: success depends on disciplined governance, well-designed schemas, and skilled implementation.
Habu is positioned as a clean room software layer that can work across different data environments, focusing on enabling collaboration and activation with privacy controls. It can be attractive for teams that want a vendor-led operating model for partner onboarding and standardized workflows.
Strengths: partner-friendly collaboration patterns, emphasis on activation use cases and interoperability.
Watch-outs: validate how policy enforcement works across clouds and how outputs are constrained to meet your risk tolerance.
LiveRamp Safe Haven is commonly evaluated by teams that need identity resolution and collaboration capabilities tied to a broader data connectivity ecosystem. It can support use cases like audience building, suppression, and measurement across partners, depending on configurations and permitted data use.
Strengths: strong connectivity and identity-related workflows, partner ecosystem advantages.
Watch-outs: carefully assess governance controls, data residency needs, and how identity choices align with your consent strategy.
What to ask in every vendor review (and insist on written answers):
- What prevents re-identification through repeated querying? Ask about differential privacy, query throttling, noise injection (if used), and minimum cohort sizes.
- Can we enforce our own policies? For example, blocking sensitive categories, restricting joins, and requiring approvals for exports.
- How is consent and purpose limitation handled? The platform should support tagging, segmentation, and governance aligned to your consent framework.
- What is the operational burden? Onboarding time, data mapping, and the level of technical skill needed to run analyses matter as much as feature checklists.
Privacy, Governance, and Security Controls to Validate Before You Buy
EEAT-aligned procurement means you can explain, in plain terms, how the system reduces risk—and you can prove it with evidence. Before contracting, involve privacy, security, and data governance stakeholders early and run a structured assessment.
Key controls to validate:
- Access management: role-based access control, least-privilege defaults, support for SSO, and strong separation between partner roles.
- Data handling: encryption in transit and at rest, key management options, data retention controls, and clear policies for deletion and partner offboarding.
- Output privacy: aggregation thresholds, k-anonymity-like protections, and restrictions on exporting user-level data. If the vendor permits row-level exports, require a detailed justification and compensating controls.
- Audit logs: immutable logs covering data access, queries, exports, and admin actions, plus alerting for suspicious activity.
- Compliance alignment: support for DPIAs, vendor risk questionnaires, and regional data residency requirements where relevant to your operations.
Answering a common follow-up question: Does a clean room remove the need for consent? No. It reduces risk by limiting exposure, but legal bases and consent signals still govern what data can be used, for which purpose, and for how long. A strong clean room supports consent-aware segmentation so you don’t mix incompatible data permissions.
Implementation Best Practices for Clean Room Marketing Measurement
Many clean room initiatives stall due to unclear ownership and unrealistic expectations. To get durable value, treat the clean room as a program, not a tool.
1) Start with two high-impact use cases
- Closed-loop measurement: connect media exposure to conversions with approved aggregation, then standardize reporting definitions.
- Suppression and efficiency: reduce wasted spend by excluding existing customers or recent converters in privacy-safe ways, where permitted.
2) Define a measurement taxonomy
Agree on consistent definitions for conversions, attribution windows, and deduplication logic across partners. Without this, clean rooms can produce “accurate” numbers that still conflict because inputs and definitions differ.
3) Build a governance workflow that people will follow
- Pre-approved query templates for common analyses
- Clear approval paths for new partner collaborations and any export requests
- Documentation that explains what is allowed and why
4) Plan for identity constraints
Assume match rates will vary. Design experiments that remain valid even with partial matches, and use incrementality tests to validate whether better targeting truly drives lift rather than simply finding people who would convert anyway.
5) Operationalize partner onboarding
Create a repeatable checklist: data schema mapping, privacy review, security sign-off, test queries, and reporting validation. This prevents each new partner from becoming a custom project.
FAQs About Digital Clean Room Platforms
Do digital clean rooms allow audience targeting without cookies?
They can support privacy-safe audience creation using first-party signals and partner data in a governed environment, but the activation method depends on the platform. Some allow exporting approved audience artifacts to specific destinations, while others restrict use to in-platform targeting.
What’s the difference between a clean room and a CDP?
A CDP centralizes and activates a company’s first-party customer data. A clean room is designed for collaboration between parties with strict controls on what can be learned or exported. Many organizations use both: the CDP for internal activation and the clean room for partner measurement and joint analytics.
Can a clean room support incrementality testing?
Yes, many can support holdout-based or experiment-driven measurement, but you should verify how randomization is implemented, what outputs are permitted, and whether the reporting provides statistical confidence indicators appropriate for decision-making.
How do we choose between walled garden and warehouse-based clean rooms?
Use walled garden solutions when you need native measurement and activation inside a specific ecosystem. Use warehouse-based solutions when you need cross-partner analytics, more customization, and closer integration with your enterprise data. Many teams use both, with a shared governance framework.
What data should we avoid putting into a clean room?
Avoid any data you cannot justify under your consent and purpose limitations, as well as sensitive categories unless you have a clear legal basis and strict controls. Even in a clean room, you should minimize data to what’s necessary for the use case.
What should we ask vendors about privacy protections?
Ask about aggregation thresholds, defenses against differencing attacks, export restrictions, audit logs, access controls, retention policies, and how the platform supports consent-aware governance. Request documentation and, where possible, evidence from security reviews.
In 2025, clean rooms are most effective when you treat them as a governed collaboration layer, not a standalone targeting hack. The best platform depends on whether you need in-ecosystem measurement, cross-partner analytics, or both, and on how rigorously privacy controls are enforced. Choose tools that default to safe outputs, provide strong auditability, and fit your operational model—then scale use cases systematically for durable results.