Delegation of computation for machine learning

这项研究属于机器学习和量子计算领域的福尔斯。机器学习在我们的日常生活中无处不在，从Spotify推荐到估计你今天的通勤时间。将机器学习任务委托给云中更强大的第三方（例如）提供了使更困难的学习问题易于处理的可能性，这是一个非常有吸引力的前景。然而，目前我们盲目地相信云计算中的第三方做得很好。这对于不太敏感的任务（如歌曲推荐）来说很好，但在更敏感的情况下就不太理想了。这项研究的目标是产生协议，消除这种盲目信任的需要，而是让我们能够验证第三方确实做得足够好。我们的目标是开发一种如何委托机器学习的理论，即要求云中的一台强大的计算机为您执行机器学习任务。这将包括开发协议，允许机器学习任务以不需要信任的方式进行委托，使我们能够验证云中强大的计算机告诉我们的内容实际上是真实的。我们的目标是为这个代表团制定透明，隐私保护和后量子安全的协议。这项工作是高度跨学科的，包括加密证明和机器学习领域。这种结合是一个新的研究方向。我们将使用密码学证明理论中的深层技术工具来解决通过新方法委托机器学习的问题。将机器学习任务委托给更强大的计算机是一个有吸引力的前景。然而，理想情况下，我们宁愿不相信说，强大的计算机正在做一个很好的工作，而宁愿有一些方法来验证这个事实为我们自己。这项工作的目的是制定协议，消除这种信任需求，并以可验证，透明，隐私保护的方式促进机器学习任务的委托。这些协议可以立即应用于任何委托的机器学习任务，其中输出足够高的质量是很重要的。双方都将从中受益，因为进行验证的一方可以保证他们接收到的是符合标准的，而进行学习的一方现在有办法说服任何人他们正在做得很好。这项工作福尔斯机器学习和量子信息领域。它着眼于这两个领域的学习授权领域，这是EPSRC的优先领域，它努力产生重大的工业和社会影响。

This research falls into the areas of machine learning and quantum computing. Machine learning is ubiquitous in our daily lives, from Spotify recommendations to estimating how long your commute is going to take today. Delegating machine learning tasks to a more powerful third-party in the cloud (for example) offers the potential to make more difficult learning problems tractable, which is a very attractive prospect. At present, however, we blindly trust that the third-party in the cloud is doing a good job. This is fine for less sensitive tasks such as song recommendations, but is much less desirable in more sensitive situations. This research will aim to produce protocols that remove this need for blind trust,and instead allow us to verify that the the third-party has indeed done a sufficiently good job.We will aim to develop a theory of how to delegate machine learning, i.e. asking a powerful computer in the cloud to carry out a machine learning task for you. This will include the development of protocols which allow machine learning tasks to be delegated in a way which doesn't require trust, allowing us to verify that what the powerful computer in the cloud is telling us is in fact true. We will aim to produce protocols for this delegation which are transparent, privacy-preserving and post-quantum secure.This work is highly interdisciplinary, comprising the areas of cryptographic proofs and machine learning. This combination is a new direction of research. We will use deep technical tools from the theory of cryptographic proofs to approach the problem of delegating machine learning via novel methodologies.Delegating machine learning tasks to a more powerful computer is an attractive prospect. However, ideally, we would prefer not to have to trust that said powerful computer is doing a good job, and would rather have some way of verifying this fact for ourselves. The aim of this work is to produce protocols which remove this need for trust, and facilitate the delegation of machine learning tasks in a verifiable, transparent, privacy-preserving way. These protocols could immediately be applied to any delegated machine learning task in which it is important that the output is sufficiently high quality. Both sides stand to gain from this, as the side doing the verifying has a guarantee that what they are receiving is up to scratch, while the side doing the learning will now have the means to convince anyone that they are doing a good job.This work falls within the areas of machine learning and quantum information. It looks at these two areas with respect to the area of delegation of learning, which are priority areas for EPSRC, and it strives to make a significant industrial and societal impact.External Partner; StarkWare