Security-first Federated Quantum Machine Learning for Genomics

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Over the past decade, there's been an enormous explosion of Machine Learning (ML) use cases across multiple industries, healthcare included. However, for many key organisations with sensitive private datasets, such as NHS Trusts, the mainstream/generic centralised ML training doesn't provide the necessary assurances in terms of the privacy and security. This is where an emerging ML paradigm, known as Federated Learning (FL), comes into play. Usefully, FL allows multiple parties, that don't necessarily trust each other, to collaborate on training a common machine learning model, all without having to share their data with each other. Thus this technology fundamentally addresses the problem of data privacy and security.Nevertheless, a crucial detail to note is that, while FL is a robust solution when it comes to; data access, governance, and ownership, it does not guarantee security and privacy unless combined with other security add-ons. Thus, FL is subject to some cyber security attacks, an example of which is if the local training datasets are not encrypted, attackers can steal personally identifiable data directly from the training nodes, or interfere with the communication process via the classical technique of a poisoning attack. Moreover, in a normal FL setup, the models are also not encrypted, leaving them open to adversarial attacks, including extraction of sensitive training data from attacks on the models.Now, a natural question one may ask is, what can be combined with FL to make it a viable solution in the healthcare space? The answer is, yes, and our proposal for this project is to supplement FL with two emerging technologies:1. Fully-Homomorphic-Encryption (FHE): In a nutshell, FHE is a novel computational paradigm that allows computation to be applied to encrypted datasets i.e. directly to cipher-text, without any decryption before/during/after the computation. The results of the computation, once decrypted, should in practice be identical to a situation in which it was applied to unencrypted data.2. Quantum-Machine-Learning (QML): In effect, QML sits at the intersection between Quantum Computing and ML. In our case, this is aimed at exploiting quantum mechanical properties, including; superposition and entanglement to build better and faster algorithms.In the healthcare sector, this solution would provide even greater security assurances, significantly lowering information governance barriers when sharing sensitive data with third-parties. This would thereby naturally enhance; collaboration, service innovation, and patient outcomes, without compromising data integrity & security. In-brief, we're aiming to address the unmet need for privacy-enhancing ML.