AWS Clean Rooms features
Create clean rooms in minutes. Collaborate with your partners without sharing raw data
Why AWS Clean Rooms?
Create your own clean room, add participants, and start collaborating in a few steps

Collaborate any company without sharing or revealing underlying data

Protect underlying data with a broad set of privacy-enhancing controls for clean rooms

Link and match customer records, use flexible analytics tools, and train and deploy ML models with your partners

Page topics
Multiparty
Open allCollaborate with multiple parties
Collaborate on your data where it lives
Open allFull programmatic access
Open allFull programmatic access
Configurable roles
Open allConfigure collaboration member roles
AWS Entity Resolution on AWS Clean Rooms
Open allRule-based and data service provider-based matching
PySpark
Open allUse PySpark for data processing and analysis
Flexible SQL
Open allFlexible SQL queries subject to analysis rules
Analysis rules are restrictions that give you built-in control of how your data can be analyzed. Collaboration members who create or join a collaboration as designated query runners can write queries to intersect and analyze your data tables subject to the analysis rules that you set. AWS Clean Rooms supports three types of analysis rules: aggregation, list, and custom.
Aggregation analysis rule: The aggregation analysis rule allows you to run queries that generate aggregate statistics, such as how large the intersection of two datasets is. When using the aggregation analysis rule, you can enforce that only aggregation queries can be run on your data and enforce restrictions on specific parts of the queries that run, such as what columns must be used only in a blind match and what columns can be used in aggregations such as sums, counts, or averages. You also control the minimum aggregation constraint in the output. You can also set minimum aggregation constraints that allow you to set conditions for output row returns. These constraints are in the form of COUNT DISTINCT (Column) >= Threshold. If an output row in the query results does not meet any of the constraints it is removed for the result set. This helps you ensure that minimum aggregation thresholds are automatically enforced while providing flexibility to data collaborators who can write queries of their choice.
List analysis rule: The list analysis rule allows you to run queries that extract the row-level list of the intersection of multiple datasets, such as the overlap of two datasets. When using the list analysis rule, you can enforce that only list queries can be run on your data and enforce restrictions of the queries that run, such as what columns must be used only in a blind match and what columns can be outputted as a list in the output.
Custom analysis rule: The custom analysis rule allows you to create custom queries using most of ANSI-standard SQL, such as common table expressions (CTE) and window functions. You can also review and allow queries before collaboration partners run them, and review other collaborators' queries before they are allowed to run on your tables. When using the custom analysis rule, you can use built-in control to determine or limit, upfront, how your underlying data could be analyzed, instead of having to rely on query logs after analyses are complete. When you use custom SQL queries, you can also create or use analysis templates to store custom queries with parameters in the collaborations. This permits customers to more easily help one another in a collaboration. For example, a member who has higher SQL experience can create templates for other members to review and potentially run. It also facilitates reusable analyses in the collaboration. You can also use AWS Clean Rooms Differential Privacy by selecting a custom analysis rule and then configuring your differential privacy parameters.
Implement differential privacy in a few steps
Build queries without writing SQL code
Use cryptographic computing
You can run AWS Clean Rooms queries on cryptographically protected data. If you have data handling policies that require encryption of sensitive data, you can pre-encrypt your data using a collaboration-specific shared encryption key so that data is encrypted even when queries are run. Cryptographic computing ensures that data used in collaborative computations remains encrypted at rest, in transit, and in use (while being processed).
Cryptographic Computing for Clean Rooms (C3R) is an open source Java SDK with a CLI, available in GitHub. This feature is available at no additional charge. If you have big data, you can review the documentation to see how C3R can be integrated into Apache Spark.
This feature is the latest of a broad range of AWS cryptographic computing tools built to help you meet your security and compliance needs while allowing you to take advantage of the flexibility, scalability, performance, and ease of use that AWS offers.
Privacy-enhancing ML
Open allApply privacy-enhancing ML
AWS Clean Rooms ML helps you and your partners apply privacy-enhancing machine learning (ML) to generate predictive insights without having to share raw data with each other. AWS Clean Rooms ML supports custom and lookalike machine learning (ML) modeling. With custom modeling, you can bring a custom model for training and run inference on collective datasets, without sharing underlying data or intellectual property among collaborators. With lookalike modeling, you can use an AWS-authored model to generate an expanded set of similar profiles based on a small sample of profiles that your partners bring to a collaboration.
AWS Clean Rooms ML helps customers with multiple use cases. For example, advertisers can bring their proprietary model and data into a Clean Rooms collaboration, and invite publishers to join their data to train and deploy a custom ML model that helps them increase campaign effectiveness; financial institutions can use historical transaction records to train a custom ML model, and invite partners into a Clean Rooms collaboration to detect potentially fraudulent transactions; research institutions and hospital networks can find candidates that are similar to existing clinical trial participants to help accelerate clinical studies; and brands and publishers can model lookalike segments of in-market customers and deliver highly-relevant advertising experiences, without either company sharing their underlying data with the other.
AWS Clean Rooms ML lookalike modeling, using an AWS-authored model, was built and tested across various datasets, such as e-commerce and streaming video, and can help you improve accuracy on lookalike modeling by up to 36%, when compared with representative industry baselines. In real-world applications such as prospecting for new customers, this accuracy improvement can translate into savings of millions of dollars.