Safe external data sharing and publication are enabled by differential privacy

Assure secure external data sharing and publication, and enable increased utility for your constituents. Increase the rate of safe data publication.

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Why is differential privacy the strongest fit for external data sharing and publication?

Differential privacy is trusted for external data sharing because it applies a mathematical framework that quantitatively reduces privacy risks. The method permits safe external collaboration and data release, striking a crucial balance between protecting sensitive information and preserving data's analytical value.

Differential privacy's strength for external data sharing lies in its ability to enable organizations to leverage their data assets for collaboration without exposing individual data points. This facilitates partnerships and research opportunities that require access to sensitive data while ensuring compliance with stringent privacy regulations. By enabling data to be shared and analyzed in a privacy-preserving manner, differential privacy opens up new avenues for innovation and development, making it an indispensable tool in the modern data ecosystem.



Compliance and privacy protection

Ensure adherence to global and state-level privacy regulations. Safeguard individual data privacy, minimizing risk of data breaches or misuse.


Data utility and accuracy

Noise can be calibrated to protect privacy without significantly compromising the utility and accuracy of the data for analysis and insights.


Trust and transparency

Demonstrate commitment to privacy and ethical data use, fostering greater willingness for data sharing and collaboration.


Hal Triedman
Senior Privacy Engineer
Wikimedia Foundation
"With Tumult Labs' open source software and expertise in technical implementation, the Wikimedia Foundation team is now able to release more granular, equitable, and safe data about how readers are using our platforms."


Differential privacy supports significant additional solutions for your organization.

Unlock new data collaboration opportunities with clean rooms

Facilitate safer data sharing with partners for more accurate measurement and analysis, without compromising user privacy.

Assure safe internal data sharing and data reuse

Leverage data for innovation and improved decision-making without compromising the privacy of individuals represented in the data

Systematize disclosure avoidance

Ensure compliance with privacy laws and maintaining public trust by safeguarding personal information against unauthorized disclosure.

Enable data monetization

Use differential privacy to aggregate and analyze large datasets without compromising the integrity of individuals.

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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What impact does differential privacy have on data utility?


Differential privacy impacts data utility by introducing noise to protect individual privacy, which can reduce the precision of the data. However, when properly calibrated, it allows for meaningful analysis and insights without compromising privacy, ensuring that datasets remain useful for a wide range of applications while adhering to privacy standards.

Can differential privacy be applied to all types of data?


Differential privacy can be applied to various types of data, including numerical and categorical. The effectiveness and the specific implementation of differential privacy mechanisms can vary depending on the nature of the data and the intended use cases. For highly sensitive or complex datasets, more sophisticated differential privacy techniques may be required to ensure both meaningful data utility and robust privacy protection.

What strategies exist for applying differential privacy in publishing datasets for research purposes?


Strategies for applying differential privacy in research dataset publication involve selecting an appropriate differential privacy model (local or global), carefully calibrating the noise to balance data utility with privacy, managing the privacy budget to control the overall privacy risk, using aggregated data queries to minimize identification risks, and regularly adjusting privacy parameters as data and research needs evolve.

Unleash the power and value of your data.