PETs are a set of technologies which mitigate the risk to the individual of data privacy abuse.
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Announcements
Bitfount has joined forces with the Milner Therapeutics Institute at the University of Cambridge as an Affiliate Company. This partnership opens up a world of opportunities for our organisations to collaborate and drive innovation in HealthTech.
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Privacy in Practice
The TRE-FX project aimed to address challenges associated with executing data analysis across multiple TREs with differing geographical or governance boundaries.
Product
RETFound, developed by researchers at Moorfields Eye Hospital and University College London, is the world's first foundation model trained on retinal images (retinal foundation model). RETFound can be accessed through Bitfount's no-code platform.
We're excited to announce that Sajan Khosla has joined Bitfount's advisory board. Read the full announcement below.
Privacy-Enhancing Technologies (PETs)
Trusted Execution Environments are one mechanism for enabling multiple parties to collaboratively do computation. The security depends on the computation running in an environment that all the parties trust.
Secure multiparty computation (MPC / SMPC) is a cryptographic protocol that distributes a computation across multiple parties where no individual party can see the other parties’ data.
Federated ML model evaluation allows you to send a trained ML model you wish to evaluate to the data, rather than requiring the data to be centralised first.
Synthetic datasets are becoming increasingly popular for training artificial intelligence models in place of the original raw datasets from which they are generated. It is a computer-generated dataset sufficiently similar to an original base dataset.
Federated Learning (FL) is simply a Machine Learning (ML) setting where many clients collaboratively train a model under the orchestration of a central server while keeping the training data decentralised.
Differential Privacy addresses the paradox of learning nothing about an individual while learning useful information about a population. Differentially private techniques protect the privacy of individual data subjects by adding random noise when producing statistics.
Tokenization is the process through which one “substitutes a sensitive identifier (e.g., a unique ID number or other PII) with a non-sensitive equivalent (i.e., a ‘token’) that has no extrinsic or exploitable meaning or value”
Homomorphic Encryption methods are encryption schemes which allow mathematical operations to be performed on the underlying data whilst keeping the data in the encrypted space.
We are honoured to announce that Ronya Rubenstein has joined Bitfount’s advisory board. Ronya brings a wealth of experience and a unique industry perspective that aligns perfectly with our mission at Bitfount.
We are thrilled to announce that Bitfount has been honoured with the CogX 2023 Award for Best Innovation in Pharma! The award recognises our groundbreaking work in accelerating clinical trials for new drug discovery through the use of Federated Data Science and Artificial Intelligence.
By the World Economic Forum’s estimation, nearly 4 billion imaging procedures are performed worldwide every year with a market size of USD 40 billion in 2023. Recent advances in medical imaging have transformed patient diagnosis and treatment.
Team
Meet Biswa, our brilliant Frontend Engineer! With his unwavering dedication to crafting exceptional user experiences, Biswa has been an invaluable asset to our team.
Federated learning is reshaping the AI landscape, providing a new paradigm for training machine learning models that prioritise both data privacy and efficiency.
Find out how the emergence of AI and LLMs raises significant ethical concerns, particularly regarding bias, privacy, and accountability.
Meet João, one of Bitfount's Software Engineers, and learn about his background and his work.
A historical view of how medical AI has been deployed and how Bitfount disrupts this model by enabling a "plug & play" approach to model deployment.
We’re excited to share details of funding awarded to Bitfount by Innovate UK, the UK’s innovation agency, to expand our distributed data science platform to address one of the most broken parts of global healthcare R&D.
Meet Blaise and find out what it's like to be the CEO and Co-Founder at Bitfount
Bitfount to launch open beta enabling data custodians to make sensitive data available to data scientists and researchers for privacy-preserving analysis and AI/ML.
Engineering
How does Bitfount approach distributed data science with a novel architecture? Learn more in this article.
An explanation of how federated learning and zero-trust are compatible concepts.
Meet Bristena and find out what a Machine Learning Privacy Engineer does at Bitfount.
Meet Daniella and find out what it's like to be a Operations Manager at Bitfount.
Meet Lauren and find out what it's like doing Business Development at Bitfount.
Meet James and find out what it's like to be a Machine Learning Engineer at Bitfount.
Meet Matt our Head of Engineering, and find out what he does at Bitfount.
Meet Ali, one of the Machine Learning Engineers, and find out what he does at Bitfount.
We've raised $5M from great investors to turbo-charge our mission of giving every organisation data collaboration superpowers. Onwards!
Use of healthcare data is currently frustrated by valid privacy- and bias concerns. Privacy-enhancing technologies like federated machine learning and analytics can safely unlock the enormous value of healthcare data for the benefit of patients.
Bitfount is a federated data science and AI platform. We're on a mission to give every organisation data collaboration superpowers, without compromising on privacy, speed or ease of use. Join us!
The value of a dataset is determined not by its existence, but by how it is actually used. Bitfount enables true usage-based access control.
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