Security-first Federated Quantum Machine Learning for Genomics

在过去的十年里，机器学习(ML)的用例在包括医疗保健在内的多个行业中出现了巨大的爆炸性增长。然而，对于许多拥有敏感私人数据集的关键组织，如NHS Trusts，主流/通用集中式ML培训并不能在隐私和安全方面提供必要的保证。这就是一种名为联合学习(FL)的新兴ML范例发挥作用的地方。有用的是，FL允许不一定相互信任的多方合作训练一个共同的机器学习模型，所有这些都不必相互共享他们的数据。因此，这项技术从根本上解决了数据隐私和安全问题。然而，需要注意的一个关键细节是，尽管FL在数据访问、治理和所有权方面是一个健壮的解决方案，但除非与其他安全附加组件相结合，否则它不能保证安全和隐私。因此，FL受到一些网络安全攻击，例如，如果本地训练数据集没有加密，攻击者可以直接从训练节点窃取个人身份数据，或者通过经典的中毒攻击技术干扰通信过程。此外，在正常的FL设置中，模型也没有加密，这使得它们容易受到对手攻击，包括从对模型的攻击中提取敏感的训练数据。现在，人们可能会问一个自然的问题：什么可以与FL结合在一起，使其成为医疗保健领域的可行解决方案？答案是肯定的，我们对这个项目的建议是用两种新兴技术来补充FL：1.完全同态加密(FHE)：简而言之，FHE是一种新的计算范例，允许将计算应用于加密的数据集，即直接应用于密文，而不需要在计算之前/期间/之后进行任何解密。一旦解密，计算的结果实际上应该与将其应用于未加密数据的情况相同。量子机器学习(QML)：实际上，QML位于量子计算和ML的交叉点上。在我们的案例中，这是为了利用量子力学的性质，包括叠加和纠缠来建立更好、更快的算法。在医疗保健领域，这种解决方案将提供更大的安全保证，显著降低与第三方共享敏感数据时的信息治理障碍。这将自然而然地增强协作、服务创新和患者结果，而不会影响数据完整性和安全性。简而言之，我们的目标是解决隐私增强ML的未满足需求。

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.