CLOUD SERVICE · AWS

AWS Clean Rooms - AWS Clean Rooms is a managed service for matching, analyzing, and collaborating on datasets without sharing or revealing the underlying data to other participants.

AWS Clean Rooms lets organizations collaborate on data analysis while retaining control over their source datasets and permitted uses. It provides collaboration management, SQL analysis controls, privacy-enhancing capabilities, and specialized workflows for machine learning and identity matching.

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What is AWS Clean Rooms

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AWS Clean Rooms provides a controlled environment in which organizations can analyze their combined datasets without giving one another direct access to the source records. Typical applications include measuring advertising outcomes, evaluating overlapping customer populations, and analyzing relationships between partner datasets. Participants agree on collaboration membership, responsibilities, permitted analyses, and which members can receive results.

For a common SQL-based implementation, each data owner prepares data in Amazon S3, registers its metadata in the AWS Glue Data Catalog, and creates a configured table in AWS Clean Rooms. The configured table identifies the permitted columns and analysis controls. The owner then associates that table with a collaboration. Creating the association does not grant other members general-purpose access to the S3 bucket; AWS Clean Rooms accesses the data through the permissions supplied for the workflow.

Analysis rules are central to the security design. Aggregation rules support approved aggregate calculations and constraints such as minimum aggregation thresholds. List rules support controlled record-level overlap analyses, so engineers must not assume that every collaboration returns only aggregate data. Custom rules provide more flexible analysis while allowing data owners to restrict which analyses or analysis templates may run. Select the rule type according to the intended output and the sensitivity of the participating data.

Analysis templates let participants review and approve parameterized SQL before execution. This is useful when analysts need to repeat a measurement with different dates or campaign identifiers without changing the approved query structure. AWS Clean Rooms differential privacy can provide additional protection for supported aggregate analyses by introducing calibrated noise and enforcing a privacy budget. Minimum group sizes alone are not equivalent to differential privacy, and repeated-query behavior should be considered when defining controls.

Successful deployment requires more than configuring the collaboration. Participants need compatible join keys, consistent data types, agreed time windows, and documented handling of duplicates and missing identifiers. They should validate joins with representative data before relying on production measurements. Source-data permissions, service access roles, output-bucket policies, and encryption settings must also be tested together.

Result delivery is a separate security boundary. Configure the authorized result recipient and destination, restrict access to generated output, and apply appropriate retention policies. Use AWS CloudTrail for API auditing and Amazon CloudWatch Logs for supported query logging. Monitor execution failures and compute consumption, and establish a process for reviewing changes to analysis rules, templates, and collaboration membership. Privacy controls reduce specific disclosure risks, but they do not replace data-use agreements or a review of whether a proposed analysis is appropriate.

Key AWS Clean Rooms Features

Cross-organization collaborations

Create collaborations with defined participants, data contributions, and result-receiving responsibilities. Organizations can contribute controlled access to their data without granting partners direct access to their source storage.

Configured tables and analysis rules

Expose approved columns through configured tables and govern their use with aggregation, list, or custom analysis rules. Choose rules based on whether the intended result is an aggregate, an allowed record-level overlap, or a more flexible approved analysis.

SQL analysis

Run supported SQL analyses across associated datasets without building a shared warehouse that gives every participant access to all source records. Validate supported SQL constructs and rule restrictions before porting existing analytical queries.

Parameterized analysis templates

Define reusable SQL templates that data owners can review and approve. Parameters allow controlled variations, such as reporting periods, without requiring analysts to rewrite the approved query.

Differential privacy

Apply differential privacy to supported analyses using configured privacy parameters and a privacy budget. Plan for the trade-off between privacy protection and result accuracy, particularly for small populations.

Machine learning collaboration

AWS Clean Rooms ML supports privacy-enhanced machine learning collaboration, including audience modeling workflows. Treat these workflows as separate implementations with their own data preparation, permissions, supported configurations, and costs.

Identity mapping support

Use integrations with AWS Entity Resolution to support ID mapping workflows for datasets that do not share the same identifiers. Identity matching requires explicit configuration and permissions; it is not automatically performed for every SQL join.

Audit and query observability

Use AWS CloudTrail to audit service API activity and enable supported query logging to Amazon CloudWatch Logs. Restrict access to logs because query text and execution metadata can contain sensitive business information.

AWS Clean Rooms Use Cases

Advertising conversion measurement

Join advertiser conversion data with publisher exposure data to calculate approved campaign metrics. Apply aggregation constraints and carefully define attribution windows, identity keys, and duplicate handling.

Customer overlap analysis

Measure the overlap between partner customer populations without exchanging complete customer lists. Use aggregate outputs when only overlap counts or segment statistics are required.

Retail and supplier analytics

Analyze relationships between retailer sales and supplier product or campaign data. Agree on product identifiers, reporting granularity, and minimum population sizes before running shared measurements.

Audience modeling

Use supported AWS Clean Rooms ML workflows to develop audience models from collaboration data. Evaluate model quality and privacy requirements separately from the controls used for SQL analysis.

Approved recurring partner reporting

Create parameterized analysis templates for recurring reports, such as monthly campaign performance or regional customer overlap. Keep query structure controlled while allowing approved reporting parameters to change.

AWS Clean Rooms pricing models

Differential privacy charges

Differential privacy has additional pricing beyond the underlying SQL analysis compute. Include its applicable privacy-protection charges when estimating the cost of collaborations that enable this capability.

Machine learning workflow charges

AWS Clean Rooms ML is priced separately from SQL analysis, with charges determined by the selected workflow and its compute or processing usage. Estimate model-related activities independently rather than applying SQL processing rates.

SQL analysis compute

SQL analysis is metered using Clean Rooms Processing Unit capacity consumed during query execution. Dataset size, query complexity, execution duration, and compute configuration affect cost; configure the collaboration's query-payment responsibilities before production use.

Supporting AWS service charges

Budget separately for services used around the collaboration, including Amazon S3 storage and requests, AWS Glue catalog operations, logging, encryption keys, and applicable data transfer. AWS Entity Resolution workflows can also introduce separate charges.

Services AWS Clean Rooms integrates with

A common architecture keeps each participant's source files in its own Amazon S3 buckets and describes those files through the AWS Glue Data Catalog. Participants create configured tables, apply analysis rules, and associate approved datasets with an AWS Clean Rooms collaboration. Dedicated IAM roles grant the service the access required for each workflow without granting partners unrestricted access to the underlying storage.The authorized result recipient writes analysis output to a controlled S3 destination. Engineers should manage this destination as a separate data product, with explicit encryption, access policies, retention, and downstream processing permissions. Output access deserves particular attention when list analyses or other approved record-level results are permitted.CloudTrail provides API-level audit records, while supported CloudWatch query logs help diagnose execution problems. Where partners lack compatible identifiers, AWS Entity Resolution can support an ID mapping workflow before collaborative analysis. Across these integrations, establish ownership for schema changes, mapping refreshes, permission updates, and cost monitoring so that a successful initial query becomes a maintainable production workflow.

AWS Glue

AWS Glue Data Catalog supplies table metadata for the standard S3-backed SQL workflow. For example, register a partitioned conversion dataset in the catalog and reference that table when creating an AWS Clean Rooms configured table. Keep schemas and partition metadata aligned with the underlying files.

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AWS CloudTrail

CloudTrail records AWS Clean Rooms API activity for security auditing and change investigation. For example, use its events to investigate who changed a configured table or modified collaboration-related resources. Combine these records with query logs rather than treating API events as a complete history of analytical output.

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Amazon CloudWatch

Supported query logging can publish execution information to CloudWatch Logs. For example, an operations team can inspect failed analyses and correlate execution behavior with changes to table configuration. Set log retention and limit access to query text and associated metadata.

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Amazon S3

Amazon S3 commonly holds the source datasets and receives analysis results. For example, each participant can retain its own Parquet files in a separately controlled bucket while the authorized result recipient uses another bucket for generated reports. Configure bucket policies, encryption permissions, and output retention explicitly.

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AWS Entity Resolution

AWS Entity Resolution supports identity mapping workflows used with AWS Clean Rooms. For example, partners with different customer identifier namespaces can configure an authorized mapping workflow and use the resulting ID mapping table in supported collaboration analyses. Define matching rules, access permissions, and mapping refresh procedures before depending on the output.

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