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Revealing Wikipedia usage data while protecting privacy
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Wikipedia’s volunteers want a systematic way to prioritize where to focus their work. Which entries are being read most? By which readers where? DP was the technology that solved for the twin, and potentially contradictory, goals of privacy preservation and actionable insights.
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summary
Empowering Wikipedia editors to make decisions on what to edit next
Regularly-updated usage data is vital for Wikipedia editors seeking to make data-driven decisions about which articles to edit next.
Daily usage data - by subject and geography - is also used by researchers of internet behavior, and the information landscape.
Open data is part of the Wikimedia Foundation’s ethos. However, transparency - in particular with respect to the location and behavior of readers - can put individuals’ privacy at risk. DP was sought as the technology that could surface insight while preserving user privacy.
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key outcomes
Solutions
DP can unlock the power of your data throughout your enterprise.
Expand the audience that can safely work with data.
Manage data-reuse over time without privacy risk.
Mathematically-assure the protection of individuals’ data.
goals
Publish more data
When data benefits various stakeholders, making more of it available adds value to numerous communities.
Publish data more frequently
Sharing data more often can enhance its utility.
Meet the Open Data mandate, safely
Ensure data is shared widely while maintaining strict standards of privacy and security.
our process
A collaborative, calibrated process to assure utility while maintaining privacy
Proven with industry leaders in the private and public sector, our process delivers on your specific goals.
the results
How do Wikipedia editors decide which parts of the online encyclopedia to improve? Like in most modern organizations, an essential part of the decision-making process relies on data. Wikipedia’s editor community seeks to understand which parts of the site are most engaged with, and by whom. This information helps them to prioritize where to add content and make other improvements. But this information can put user privacy at risk.
Wikipedia sought Tumult’s help in applying DP in a way that would maintain privacy while providing actionable insights.
More data released
Wikimedia is better able to meet its mission around Open Data
More frequent data releases
Editors gain clearer insights of where to put their attention
Safer data releases
Data serves researchers without risking privacy
More resources
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Illuminating college outcomes, while protecting privacy
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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.
Evaluating the usability of differential privacy tools with data practitioners
Differential privacy (DP) has become the gold standard in privacy-preserving data analytics, but implementing it in realworld datasets and systems remains challenging.
Tumult Analytics: a robust, easy-to-use, scalable, and expressive framework for differential privacy
Tumult Analytics is an open-source framework for releasing aggregate information from sensitive datasets with differential privacy (DP).