How to Build a Data Clean Room: AWS Clean Rooms, Snowflake, and Privacy-Preserving Data Collaboration

The specific questions clean rooms exist for, audience overlap, campaign attribution, patient or customer cohort matching, all require joining two organizations' data on some shared identifier. Doing that with a normal data export or a shared database with row-level access defeats the point of the exercise: whichever party ends up holding the raw joined rows now has data governance obligations, breach exposure, and reuse risk it never had before, and there is no technical mechanism left to enforce the original agreement about what the data can be used for. A clean room instead runs the query inside a boundary neither party fully controls alone, and enforces, at the platform level, that only results meeting an agreed shape (an aggregate above a minimum threshold, a list restricted to a specific join, or a pre-approved custom query) are permitted to leave that boundary. Our privacy engineering guide covers the broader discipline this sits inside; this guide focuses specifically on the clean room implementation itself.

• Data governance and legal sign-off on both sides. A clean room is still a data-sharing arrangement, not a way to route around one; confirm the agreement covers exactly what will be joined, how results can be aggregated, and what either party may do with a derived output afterward.
• Schema alignment on a shared join key. Both parties need a common identifier, typically a hashed email or hashed device ID produced with a mutually agreed hashing method, before configuring any analysis rule. The clean room's controls govern how the joined query runs; they do not fix an inconsistently hashed or inconsistently formatted join key arriving on either side.
• A platform decision: AWS Clean Rooms, Snowflake's secure-view-and-policy pattern via Secure Data Sharing, or a dedicated third-party clean room vendor. The right choice tracks your team's existing stack and use case rather than any universal best answer, which the following two sections cover concretely.
• An agreed minimum aggregation or cohort-size threshold, decided with your counterparty before the first query is written, not improvised per analysis once results start coming back smaller than expected.

1. Create a collaboration in AWS Clean Rooms and invite your counterparty's AWS account as a member.

2. Each party registers its own data as a configured table, sourced from its own AWS Glue Data Catalog or S3 location. The underlying rows never leave that party's own AWS account or get copied to the other side.

3. Each party selects and configures an analysis rule type on its configured table. Aggregation rules permit COUNT, SUM, and AVG queries with a minimum aggregation threshold on the join key. List rules permit row-level overlap lists but restrict the query to an inner join with no ability to pull arbitrary columns. Custom rules permit close to full SQL, gated behind mandatory analysis template review and, optionally, differential privacy or minimum aggregation thresholds. Note that a query can only reference configured tables that share the same analysis rule type.

4. If using the custom rule with analysis templates, submit the analysis template for your counterparty's review and approval. A query outside an approved template will not run, which is the mechanism that lets a party review exactly what SQL will execute against its data before ever granting access.

5. Set an output constraint per party, for example requiring at least 150 distinct identifiers in a result row. AWS Clean Rooms automatically enforces the more restrictive of the two parties' constraints whenever a query references configured tables from both sides.

6. Run the collaboration query from an authorized member account and confirm the results returned match only the aggregated or list output the configured rule type permits, with no unexpected columns or row-level detail included.

1. Each party keeps its data inside its own Snowflake account. Use Snowflake's Secure Data Sharing to share a specific object, not a full database, with your counterparty's account, without copying or moving the underlying rows.

2. Build a secure view on top of the shared data that expresses only the joined, aggregated question actually needed, for example an overlap count by campaign, rather than a row-level join, and grant the counterparty access to that secure view instead of the base tables.

3. Apply a row access policy to the base tables or the view so a given query can only see rows meeting criteria you define, such as excluding rows below a cohort-size threshold or scoping visibility to a specific approved analysis.

4. Apply a masking policy to any column that could re-identify an individual if the result is later cross-referenced elsewhere, such as a raw email, device ID, or name, so that even a party with legitimate query access to a permitted view sees a masked or tokenized value, never the original.

5. Snowflake does not ship a dedicated clean-room differential-privacy primitive as a documented first-class feature the way AWS Clean Rooms' custom analysis rule does. Implement your own minimum-aggregation-threshold check directly inside the secure view's SQL, for example a HAVING COUNT(DISTINCT id) >= N clause, rather than assuming the platform enforces this for you by default.

6. Document and version-control the secure view, row access policy, and masking policy definitions. These are the actual controls your counterparty is trusting, and any change to them needs the same review and sign-off as the original collaboration agreement.

Set a minimum aggregation threshold based on the smallest cohort you are willing to have re-identified if an adversary combines several query results, not just the smallest cohort a single query returns on its own. A single query might safely require 150 distinct users per output row, but if a counterparty can run many differently filtered queries and mathematically combine the outputs, the effective anonymity across that set of queries can be far lower than the stated per-query threshold suggests.

Restrict which columns are permitted in output at all, using AWS Clean Rooms' disallowed output columns and comparison controls or an explicit column allowlist in a Snowflake secure view, rather than relying on aggregation alone. Some column combinations, a rare zip code paired with an age, for instance, can re-identify a specific person even after the query technically aggregates.

Log and review every query run against the clean room, including the ones that return no result. A denied or empty-result query is itself information, since it tells the requester the cohort fell below threshold, and a party running many such queries in a short sequence is worth flagging regardless of which platform you use.

Restrict who can modify the analysis rule, secure view, or policy definitions to a small, named group on each side, kept separate from who is authorized to run approved queries. That separation means an accidental or malicious change to the rules themselves requires more than one person's access to slip through unnoticed.

Deliberately write a query designed to extract row-level data through an aggregation-rule table, for example a GROUP BY on a near-unique combination of columns intended to return exactly one row per group, and confirm the platform blocks or suppresses it rather than merely returning a warning alongside the data. Build a test cohort with a known size below your configured minimum aggregation threshold and confirm the query returns no result, or a clearly marked suppressed result, instead of a real, small number that would identify that cohort. Run the same underlying business question as two or three separately filtered queries and check by hand whether combining their results narrows the population enough to approach individual-level identification; if it does, your per-query threshold is not actually protecting the data and needs a cross-query control, not just a per-query one. Have each party independently attempt to add or modify an analysis rule, secure view, or policy without the other side's sign-off, and confirm your change-management process, not just the platform's technical controls, actually catches and blocks that attempt.

A technically compliant aggregated query can still leak individual-level information if an adversary runs several overlapping aggregate queries and subtracts the results to isolate one person's contribution, a technique generally called a differencing attack. Neither AWS Clean Rooms' per-query aggregation threshold nor a Snowflake HAVING-clause threshold stops this by default; closing it requires either a genuine differential-privacy mechanism, which AWS Clean Rooms offers as a configurable option on its custom analysis rule, or query-history-aware limits on how many overlapping queries a party is permitted to run.

The most direct way a clean room fails in practice is a party, often under deadline pressure, approving a custom analysis template or writing a secure view that permits a broader join or a lower threshold than the parties actually agreed to, effectively granting row-level access by accident. This is a process failure more than a technical one, which is exactly why query and policy review needs to be a named, enforced step rather than an assumption that someone will catch it.

A schema change on either side, a renamed join key, a changed column type, or a restructured source table, can break an aggregation rule or secure view outright, which is disruptive but safe, or, worse, silently change what a query returns without erroring at all, which is far more dangerous than a broken pipeline. Treat any schema change on either side as requiring a mandatory review of the shared analysis rules and views before the next query runs, not just an informal heads-up between teams.

Aggregation reduces re-identification risk; it does not eliminate it without genuine differential privacy controls. A minimum aggregation threshold protects against a single query pulling a too-small cohort, but it does not, by itself, protect against the differencing attack described above. Treat a clean room that has not enabled differential-privacy noise as reducing risk rather than eliminating it, and be explicit with your counterparty and in any regulatory documentation about which of the two you have actually implemented; our GDPR technical controls guide covers how this distinction maps to regulatory expectations around anonymization versus pseudonymization.

A clean room is an ongoing joint operational commitment, not a one-time setup. Every schema change, every new analysis question, and every threshold adjustment needs coordinated review on both sides, and understaffing that review process is a more common failure mode in practice than any single technical misconfiguration.

Vendor lock-in is a real consideration on both platforms. AWS Clean Rooms ties a collaboration to AWS Glue Data Catalog and account structure; Snowflake's approach ties it to both parties running Snowflake or a Snowflake-compatible sharing consumer. Moving an established collaboration to a different platform later means re-implementing your analysis rules or policies from scratch, so weigh your organization's and your counterparty's existing data platform commitments before choosing, rather than deciding on a feature checklist alone.