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Tumult Labs is a leader in differentially private technology and its applications.
CATEGORIES
Video
Newsroom
Blog post
Case Study
Research
Blog post
Don’t leave the door to your data clean room open!
Video
A short tour of Tumult Analytics
Blog post
Tumult Labs named BigQuery partner to enable data clean rooms
Newsroom
Press release: Tumult Labs Recognized in Three Gartner® Hype Cycle™ Reports for Differential Privacy
Newsroom
Press release: Tumult Labs Helps Wikimedia Foundation Ensure Privacy of Detailed Data Usage Metrics
Blog post
User-level privacy in Tumult Analytics with privacy IDs
Case Study
Publishing Wikipedia usage data with strong privacy guarantees
Blog post
PETs and you: mapping privacy-enhancing technologies to your use cases
Blog post
Bringing differential privacy to Google Cloud Platform
Blog post
What anonymization techniques can you trust?
Blog post
Differential privacy made easy… and open-source!
Video
Video: Sharing insights without leaking personal information
Research
Tiny bits matter: precision-based attacks on differential privacy
Blog post
Is differential privacy the right fit for your problem?
Research
Benchmarking Differentially Private Synthetic Data Generation Algorithms
Research
Equity and Privacy: More Than Just a Tradeoff
Research
Differentially Private Algorithms for Detailed Race and Ethnicity in the 2020 Census
Research
Using Differential Privacy to Safely Release Earnings Data on Post-Secondary Grads
Case Study
How Tumult Labs helped the IRS support educational accountability with differential privacy
Research
Customizing Differential Privacy to Meet Legal Interpretations of Privacy and Deliver Accurate Data
Research
An Innovative Programming Framework for Authoring Accurate, Efficient and Private Algorithms
Research
PrivateSQL: Reimagining and Designing a New Differentially Private SQL Query Engine
Research
HDMM: Automatic Optimization for Accurately Answering Sets of High-Dimensional Queries Under Differential Privacy
Research
The Question of Fairness: Our proposal for Improved DP Algorithms in Order to Strengthen Equity
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