Safe data collaboration and clean room use are enabled by differential privacy

Empower your team to collaborate with differentially private joins and advanced data transformations.

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Why is differential privacy the missing ingredient for safe data collaboration and clean room use?

Because it enables data analysis while systematically protecting sensitive information. Differential privacy is well-suited for safe data collaboration and clean room environments because it systematically protects sensitive information while enabling data analysis.

By adding mathematical noise, it ensures individual data points are obscured, allowing organizations to collaborate and share insights without risking leaks or privacy breaches. This balance between utility and privacy fosters trust, ensures regulatory compliance, and promotes innovation by allowing safe access to valuable data insights in collaborative settings.

The strength of differential privacy in clean rooms  stems from its unique ability to adapt to various data types and analysis needs, offering a scalable solution for complex data ecosystems. Its application enhances the integrity of clean room environments by ensuring that even when data from multiple sources is combined, the privacy of individuals within each dataset is preserved.



Privacy and compliance

Individual privacy is protected and compliance with stringent data protection laws is assured, enabling collaboration to proceed.


Data utility and insight

Preserve the analytical usefulness of data, enabling insights and decision-making without compromising privacy.


Enhanced trust and security

Mitigate the risk of data leaks and build trust among collaboration partners by ensuring ethical data use and protection.


Allison Schiff
Managing Editor
Ad Exchanger
“While differential privacy began as an academic notion…ad tech companies need to know about it, too, and some even see it as the future of privacy protection.”


Differential privacy supports significant additional solutions for your organization.

Assure safe internal data sharing and data reuse

Reduce data governance overhead and enhance data-driven decision making and innovation.

Guarantee safe external data sharing and publication.

Enable responsible data sharing and monetization.

Enable data monetization

Access new revenue opportunities while maintaining regulatory compliance.

Systematize disclosure avoidance

Safeguard sensitive information; reduce the risk of privacy breach.

case studies

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Illuminating college outcomes, while protecting privacy

Public Sector

Joining sensitive data sets from the Department of Education and the IRS in a way that protected privacy resulted in College Scorecard - a platform that allows students and families to simultaneously consider the cost and evidenced outcomes of a range of possible degrees.

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How does differential privacy ensure data security in collaborations and clean rooms?


Differential privacy ensures data security in collaborations and clean rooms by adding controlled, randomized noise to the data or queries, which masks individual entries. This process prevents the identification of individuals from the shared data, allowing only aggregate information to be analyzed. By doing so, it supports secure data sharing and analysis within a controlled environment, protecting sensitive information while enabling collaborative insights.

Can differential privacy be tailored to different levels of data sensitivity?


Yes, differential privacy can be tailored to accommodate varying levels of data sensitivity. This flexibility allows organizations to apply stronger privacy measures to more sensitive data, adjusting the amount of noise added based on the privacy requirements of different datasets. This customization ensures that privacy protections are proportional to the sensitivity of the information, enabling effective data protection while preserving the utility of the data for analysis and decision-making.

How do we measure the impact of differential privacy on data utility in collaborative settings?


Measuring the impact of differential privacy on data utility in collaborative settings involves analyzing the accuracy of aggregated data outcomes against original data sets. This process includes evaluating the statistical significance of results derived from differentially private data, comparing them with those obtained from non-private data. Commonly employed techniques include utility loss metrics, which quantify the deviation in data utility due to noise addition, and user studies, assessing the practical usefulness of the data for specific applications, are commonly employed. This ensures a balance between privacy protection and the data's value for analysis.

What are the implications of differential privacy for data sharing agreements?


The adoption of differential privacy in data sharing agreements mandates clear stipulations on privacy standards, including the level of data anonymization and the privacy budget. It necessitates defining responsibilities for applying differential privacy mechanisms and ensuring compliance with privacy laws, potentially altering how data is shared, accessed, and used by parties. This framework ensures both parties adhere to high privacy and data protection standards, fostering trust and facilitating safer data exchanges.

Unleash the power and value of your data.