A federated privacy-preserving platform for solving data collaboration challenges. From discovering and evaluating third-party datasets to running data consortia, training advanced AI models, and much more.
How it works
Bitfount is a secure network for sending algorithms to data instead of the other way around. Whether you're connecting a dataset or sending an algorithm, get up and running securely in minutes not months.
Developer-friendly. Data-Owner-friendly. Data-Protection-Officer-friendly.
Other federated frameworks are cumbersome and complicated. Bitfount handles all the complexity so you can focus on getting things done.
Simple Python API (also available as Docker image)
SaaS platform for data discovery, access control, account administration and more.
Single Sign-On (SSO) as standard
Bitfount handles all fully-encrypted orchestration. Zero trust needed.
Comprehensive user-friendly documentation
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We've worked hard to make it easy to get up and running without compromising on functionality and security.
Turnkey setup and integrations. Plays nice with your existing tools.
From Federated Learning to SQL
Support for tabular and image data. Text and audio coming soon
Secure Multi-Party Computation out of the box
Coming soon: Differential privacy, homomorphic encryption and more
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The value of data is inseparable from the way it's processed. Bitfount Pods (Processing on Data)™ give data owners control over both which data and which algorithms any given collaboration partner can use.
Securely connect and access data in minutes
Link siloed datasets while preserving privacy
Full compatibility with on-premise data. No cloud migration needed.
Maintain granular, role-based, time-bound control of your data
Control your Pods' visibility: from publicly discoverable to fully private.
Includes support for custom machine-learning models
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The current need to centralise data for machine learning and analysis stifles innovation, reinforces monopolies and puts sensitive data at risk. The Bitfount platform is designed for large-scale and secure dataset evaluation, federated machine-learning and analytics.
Unlocking the value of data that can't be shared in raw form
No centralised and vulnerable datastore
Enables data owners to monetise their datasets
Promotes data interoperability
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