Tumult Analytics is an advanced privacy solution designed for robust production use. It is scalable and easy to use, providing familiar Python APIs similar to Pandas and PySpark. This platform supports a wide range of aggregation functions, data transformation operators, and privacy definitions, allowing users to protect sensitive data effectively.

Features:

• Robust and Production-Ready: Built for large-scale applications, Tumult Analytics ensures privacy at scale.
• Scalable: Capable of handling input tables with billions of rows, making it suitable for large-scale applications.
• Easy to Use: Provides familiar Python APIs, simplifying the adoption process.
• Powerful: Supports a wide range of aggregation functions, data transformation operators, and privacy definitions.

Benefits:

• Protects Sensitive Data: Ensures privacy while preserving the accuracy of data analysis.
• Scalability: Suitable for large-scale applications, handling input tables with billions of rows.
• Ease of Use: Familiar Python APIs make it easy to adopt and use.
• Flexibility: Wide range of aggregation functions, data transformation operators, and privacy definitions available.

Use Cases:

• Data Analysis: Performs public and private joins, filters, and user-defined functions on data.
• Metric Computation: Computes various metrics, including counts, sums, quantiles, and more, while preserving privacy under multiple privacy models.
• Privacy Foundation: Tumult Core serves as the sophisticated privacy foundation on which Tumult Analytics is built, ensuring secure access to sensitive data and providing embedded proof of privacy.