AWS Clean Rooms Differential Privacy is now generally available | Protect the privacy of your users with mathematically backed controls in a few steps »

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AWS Clean Rooms Differential Privacy

AWS Clean Rooms Differential Privacy

Protect the privacy of your users with mathematically backed controls in a few steps

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AWS Clean Rooms Differential Privacy helps you protect the privacy of your users with mathematically backed and intuitive controls in a few steps. As a fully managed capability, no prior differential privacy experience is needed to help you prevent the re-identification of your users. AWS Clean Rooms Differential Privacy obfuscates the contribution of any individual’s data in generating aggregate insights in collaboration outputs and helps you run a broad range of SQL queries to generate insights about advertising campaigns, investment decisions, clinical research, and more.

What is differential privacy?

Differential privacy is a mathematically proven framework for data privacy protection. The primary benefit behind differential privacy is to help protect data at the individual level by adding a controlled amount of randomness to obscure the presence or absence of any single individual in a dataset that is being analyzed.

However, differential privacy is not easy to implement because configuring this technique requires an in-depth understanding of mathematically rigorous formulas and theories to apply it effectively. We created AWS Clean Rooms Differential Privacy to help you protect the privacy of your users with mathematically backed controls in a few steps.

Benefits of AWS Clean Rooms Differential Privacy

Apply sophisticated mathematically proven differential privacy with no prior experience

Protect data about specific individuals in AWS Clean Rooms collaborations with state-of-the-art mathematical theorems that obscure the information about any individual in a population, with no prior differential privacy experience.

Reduce heavy lifting and auditing with a fully managed differential privacy capability

Enable differential privacy in AWS Clean Rooms collaborations in a few simple steps—all without requiring any additional setup from your partners. As a managed capability, you can decrease or supplement your reliance on preapproving or auditing queries.

Use highly flexible and configurable controls to support your specific use cases

AWS Clean Rooms Differential Privacy can be configured with flexible controls that adapt to your specific business use cases and can be applied in just a few steps.

Perform differentially private analyses using various queries

Run custom and flexible analytics including complex query patterns with common table expressions (CTEs) and commonly used aggregate functions like COUNT and SUM.

Use cases

Optimize media planning

Plan your advertising spend by determining user overlap with marketing partners without revealing which customers are in common.

Expand advertising campaign measurement

Measure the return on investment (ROI) of marketing with a media publisher to optimize campaigns based on aggregate advertising insights.

Improve auto insurance assessments

Complement auto insurance policy creation with market insights about a driving population without revealing data about individuals.

Accelerate biomedical research

Advance clinical research insights by collaborating with medical institutions without revealing information about an individual patient.

Customers and partners

Comscore is a measurement and analytics company that brings trust and transparency to media.

“We are excited to be using AWS Clean Rooms Differential Privacy as part of Comscore’s interoperability strategy for our measurement and activation solutions as we innovate with privacy in mind. This approach is critical in ensuring data collaborations that cannot be easily reverse-engineered. Thanks to AWS Clean Rooms Differential Privacy, we can collaborate with confidence as it enables us to generate aggregated insights while helping us preserve the privacy of our customers. We value AWS Clean Rooms Differential Privacy’s approach in enabling customers to apply differential privacy in minutes and for its ability to support a wide range of queries.“

Kelly Barrett, Senior Vice President of Product Management, Comscore

Kantar is a globally recognized data, insights, and consulting company that helps clients understand people and inspire growth.

“At Kantar, we recognize the importance of differential privacy to help preserve individual privacy while advancing data-driven insights. We are excited to see that AWS is the first major cloud provider investing in a fully managed and easy-to-use differential privacy capability in data collaborations. AWS Clean Rooms Differential Privacy is an essential tool in our data strategy and reinforces our dedication to safeguarding the future of measurement."

Steve Silvers, Executive Vice President, Global Creative and Media Solutions, Kantar

Samba TV is a data and analytics company focused on a next-generation television experience. It helps viewers discover and engage with relevant content, and it helps brands and agencies address and measure that engagement effectively.

“In the realm of TV advertising measurement, providing accurate performance data to our advertisers and publishers while protecting consumer privacy is crucial. Our partnership with AWS further helps us enable these goals for all of Amazon’s advertising partners, and we look forward to helping them implement AWS Clean Rooms Differential Privacy.”

Cole Strain, Head of R&D, Samba TV

Cuebiq is a leading mobility insights company, providing companies a trusted, high-quality, and transparent source for offline visitation data and tools.

“At Cuebiq, we focus on building valuable insights by processing location data about millions of anonymous consumers. As data privacy is a key priority for our customers in today’s digital landscape, we believe it is important to apply stringent privacy measures in data collaborations. AWS Clean Rooms Differential Privacy marries our goal to provide best-in-class protection to consumers and unlock petabytes of new data in a privacy-friendly environment. Knowing the value of differential privacy, we are excited to be among the first to test AWS Clean Rooms Differential Privacy as an early adopter.”

Marco Funaro, Executive Vice President of Engineering, Cuebiq

Affinity Solutions, a leader in consumer purchase insights, uses data from over 140 million cards to provide an unparalleled view of US consumer spending, transforming data into actionable insights that drive market share and revenue growth.

“At Affinity Solutions, we bear the responsibility to help safeguard each individual’s privacy while simultaneously delivering the comprehensive insights our customers expect. We are excited about our collaboration with AWS around AWS Clean Rooms Differential Privacy as it aligns with our commitment to consistently deliver accurate results for our clients while helping protect user-level privacy in our customers' and partners' datasets.”

Atul Chadha, Chief Technology Officer, Affinity Solutions

Slalom is a global business and technology consulting company.

“Privacy is a critical issue for consumers today, and our clients expect their customers’ PII to be safeguarded in data collaborations. AWS Clean Rooms Differential Privacy is a game-changer. We are thrilled to use this solution to save our clients hundreds of hours previously spent vetting partners and preapproving clean room queries. Differential privacy is going to unlock tremendous value for our clients.”