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.

Quantum computing harnesses the power of quantum mechanical properties such as superposition and entanglement, to solve problems that are beyond the reach of classical computing. There is a limit to how much more powerful our classical computers can become as suggested by that the famous Moore's Law, which posits that computing power doubles roughly every two years, and due to physical constraints involved in the further miniaturisation of transistor chips this is nearing its limit. Moreover, the speedup in computing offered by parallelization is limited by the famous Amdahl's law. This project aims to build a privacy-preserving platform for genomics datasets that harnesses the exotic properties of quantum computing such as superposition and entanglement to enable data analysis of encrypted datasets faster than what is currently possible with classical computing. Our vision for the platform is to become a ubiquitous quantum orchestration environment for developing and deploying privacy-preserving quantum algorithms for sensitive data e.g. genomics datasets. This project will also focus on establishing an early commercial pilot with; individual organisations, regional healthcare data sharing ecosystems and global healthcare economies. As per the NHS England "Five Year Forward View" and the National Information Board's "Personalised Health and Care 2020", the priorities of; data capture, mining, analysis and sharing are rightly seen as essential keys to transforming health outcomes for patients and citizens. Plus, with the potential value of health data being huge according to a recent Unilever Research report that estimates that a person's health information is 50x more valuable than their financial data. As such, cyber criminals prize health data very highly as it allows them to create very convincing false identities based on personal histories. Likewise, the public is also acutely sensitive to their personal health information being misused by businesses, which they believe could expose them to discriminatory practices. As a result of all these insights, this project represents attempts to address this global challenge which will create fantastic export opportunities for the UK. A recent report, commissioned by IBM Security, looked at the annual cost of data breaches at 419 sample organisations in 13 nations or regions. The report found that the average total cost per organisation was a staggering £2.7 million, based on an average £106 per lost or stolen record. An organisation that rigorously protects its records with our Homomorphic Encryption solution could remove these costs and associated legal, regulatory and reputational risks.

This project will demonstrate the feasibility of Zaiku's cutting edge Homomorphic Encryption research, which aims to ensure the most sensitive, valuable data can be safely stored, updated and shared in its encrypted state, so that data is never vulnerable. The feasibility study will also focus on establishing the commercial opportunity for Homomorphic Encryption across NHS services in England, selected export markets, and also in the banking and financial sectors. From the NHS England "Five Year Forward View" to the National Information Board's "Personalised Health and Care 2020", data capture, mining, analysis and sharing are rightly seen as the essential keys to transforming health outcomes for patients and citizens. This creates positive pressure for healthcare organisations to be paper-free and unlock the value of data, and poses tremendous challenges in protecting the security and confidentiality of sensitive patient information. The potential value of health data is huge. Cyber criminals prize health data highly, as it allows them to create very convincing false identities which, unlike credit cards they cannot be cancelled. The public is also highly concerned that their personal health information could be misused by businesses such as insurers, which they believe could expose them to discriminatory practices. Homomorphic encryption is a novel form of encryption theory, intended to allow searching and changing encrypted information without first decrypting it, as is currently required. The results of changes made should be the same as if they were applied to unencrypted data. This is highly innovative, especially in healthcare, where it could ease safe and appropriate sharing of sensitive data, enhancing service innovation and patient outcomes without compromising data security.

Our aim is to develop an ‘Inflatable Telepresence Drone’ called ‘InDrone’ for people with motor disabilities. InDrone will empower users to interact with other people and environments by overcoming physical barriers. Essentially, InDrone is a floating camera, microphone & speakers; making it a person’s eyes, ears and voice in another room (the user will receive live 3D video & audio). InDrone can levitate using its helium gas balloon & three covered propellers. This is completely different to other robotic telepresence devices on the market that can only venture into disabled access areas due to having wheels. The helium lightens the load, thereby increasing cost effectiveness due to reduced power consumption and as InDrone can float it can avoid obstacles on the ground, walls and ceiling i.e. climbing up & down stairs and even going vertically up. Users can control InDrone via; Smartphone App, Computer Device, Oculus Rift Headset (Goggles that allow one to see the 3D environment as if standing in the room) and EEG Neural Headset (For severe motor disabilities InDrone can be piloted via the brains neural activity). Future developments include reducing the balloon size and adding a screen so the users face can be shown.