Tumult Labs develops differential privacy technology that enables the safe sharing of de-identified data and machine learning models while ensuring individual privacy. Their solutions facilitate responsible data collaboration across various sectors, allowing organizations to extract valuable insights without compromising sensitive information.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations struggle to extract valuable insights from sensitive data due to privacy concerns and regulatory restrictions. Sharing and collaborating on data, even for internal use, can expose individuals to re-identification risks and potential privacy breaches. Traditional methods of de-identification are often insufficient to protect against modern data analysis techniques.
Solution
Tumult Labs provides a differential privacy platform that enables organizations to safely share and analyze sensitive data while guaranteeing individual privacy. Their technology transforms raw data into statistically safe, de-identified datasets using differential privacy algorithms. This allows data scientists, analysts, and researchers to unlock insights from previously inaccessible data, facilitate secure data collaboration, and enable data monetization without compromising privacy. The platform supports various use cases, including systematizing disclosure avoidance, assuring safe internal data sharing, enabling secure external data publishing, and facilitating secure multi-party collaboration through clean rooms.
Target Audience
Tumult Labs targets product leaders, data scientists, analysts, and data stewards across various sectors, including public sector, advertising, and banking and finance, who need to share and monetize insights from sensitive data while adhering to privacy regulations.
Features
- Differential privacy algorithms that add calibrated noise to data, ensuring privacy guarantees
- Tools for systematizing disclosure avoidance and reducing the risk of privacy breaches
- Capabilities for assuring safe internal data sharing and data reuse
- Features for enabling secure external data sharing and publishing
- Support for unlocking new data collaboration opportunities with clean rooms
- Functionality for generating differentially-private synthetic data
- Support for aggregate data analysis, including predictive modeling
- Tools for prototyping and deploying differential privacy easily