What is Tumult Analytics?
Tumult Analytics is a sophisticated platform designed for deploying differential privacy. Engineered by a team of differential privacy experts, the platform is robust and production-ready, with proven reliability in institutions like the U.S. Census Bureau.
Tumult Analytics is built on Spark and effortlessly supports extensive input tables, making it highly scalable. Its Python APIs, similar to Pandas and PySpark, ensure ease of use. The platform also supports a comprehensive, expanding list of aggregation functions, data transformation operators, and privacy definitions.
Features
- Robust and production-ready: Built and maintained by differential privacy experts, and running in production at institutions like the U.S. Census Bureau.
- Scalable: Runs on Spark and effortlessly supports input tables containing billions of rows.
- Easy to use: Familiar Python APIs similar to Pandas and PySpark.
- Powerful: Supports a large and ever-growing list of aggregation functions, data transformation operators, and privacy definitions.
- Powerful Transformations: Perform public and private joins, filters, or user-defined functions on your data.
- Private Aggregations: Compute counts, sums, quantiles, and more under multiple privacy models.
Use Cases
- Securely analyzing large datasets with billions of rows.
- Implementing privacy-preserving data analysis in production environments.
- Performing complex data transformations while ensuring data privacy.
- Computing various aggregations (counts, sums, quantiles) under different privacy models.
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