AF: Small: Collaborative Research: New Representations for Learning Algorithms and Secure Computation

机器学习最近的成功部分归功于用于训练和测试目的的大型数据集的可用性。然而，训练过程是计算密集型的，并且收集的数据集通常是隐私敏感的。这导致了提供机器学习即服务（MLaaS），其中数据提供商将其数据存储在云中，客户通过云购买经过训练的模型或预测。这是一个有吸引力的解决方案，但它需要数据提供商信任云。一个被破坏的云可能会使大量人口的隐私处于危险之中。针对此问题的现有加密解决方案修改了MLaaS的底层模型，而不是专门针对机器学习任务定制的。因此，需要开发安全的机器学习过程，其整体地考虑密码学和机器学习方面。该项目采用了这样一种整体方法，专注于两个主要方向：设计更密码友好的机器学习算法，以及专门用于机器学习算法的云中安全计算的新协议。因此，由于跨学科的方法，机器学习和密码学研究的艺术水平都将得到提高。该项目将支持和促进代表性不足群体的学生参与研究。该项目有两个主要方向：几乎线性机器学习组件和MLaaS拓扑上的安全计算。对于第一个研究目标，研究人员团队的目标是找到学习算法的新表示，最大限度地减少非线性运算，这对于密码算法来说是昂贵的。特别是，该项目将专注于关键机器学习组件的新设计：激活和损失函数的随机近似，可以减少训练和/或测试期间的非线性操作数量。这将直接减少安全计算期间产生的计算开销。除了深入开发这些设计原则外，该项目还将探索深度学习的某些非线性组件的并行化技术，以进一步通过并行计算来分摊密码开销。与此同时，目标将是保留修改后的机器学习算法的保证。对于第二个研究方向，重点将放在安全多方计算（MPC）的概念。安全MPC协议是一种通用方法，允许两个或多个相互不信任的方在其私有输入上计算任何函数，使得每一方除了其预期输出之外什么也不知道。传统的MPC协议是为与MLaaS拓扑大不相同的网络拓扑而设计的。因此，将设计新的MPC协议，以轻松满足MLaaS等机器学习框架的特定需求。这些结果将通过开发两种新的安全计算技术来实现。第一种技术将开发一种多密钥同态加密方案，同时是一种分散的基于属性的加密。第二种技术将开发一种新的方法来扩展用于遗忘传输的简洁协议。这些技术的成功开发也将直接推动同态加密和安全计算协议的发展。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Recent success of machine learning is due in part to the availability of large datasets for training and testing purposes. However, the training process is computationally intensive and collected datasets are often privacy sensitive. This has led to providing Machine Learning as a Service (MLaaS), where data providers store their data in the cloud and clients purchase trained models or predictions through the cloud. This is an attractive solution, however it requires the data providers to trust the cloud. A compromised cloud can put the privacy of a large population at risk. Existing cryptographic solutions to this problem modify the underlying model of MLaaS and not specifically tailored for machine learning tasks. Accordingly, there is a need for developing secure machine learning procedures, that takes into account both cryptographic and machine learning aspects holistically. This project takes such a holistic approach, focusing on two primary directions: designing machine learning algorithms that are more cryptographic-friendly and new protocols for secure computation in the cloud specialized to machine learning algorithms. As a result, both the state of the art of machine learning and cryptographic research will be advanced thanks to an interdisciplinary approach. This project will support and promote involvement of students from under-represented groups in research. This project has two primary directions: Almost-Linear Machine Learning Components, and Secure Computation over the MLaaS Topology. For the first research goal, the team of researchers aims at finding new representations of learning algorithms that minimize non-linear operations, that are costly for cryptographic algorithms. In particular, this project will focus on a new design for critical machine learning components: stochastic approximation of activation and loss functions that allows to reduce the number of non-linear operations during training and/or testing. This will directly reduce the computational overhead incurred during secure computation. In addition to developing these design principles in depth, the project will also explore techniques for parallelization of certain non-linear components of deep learning, to further amortize cryptographic overhead through batching. At the same time, the aim will be on retaining the guarantees of the machine learning algorithms under modification. For the second research direction, the focus will be on the concept of secure multiparty computation (MPC). A secure MPC protocol is a general method that allows two or more mutually distrustful parties to compute any function over their private inputs so that each party learns nothing beyond its intended output. Traditional MPC protocols are designed for network topologies that are vastly different from the MLaaS topology. Hence, new MPC protocols will be designed, to easily fit the specific needs of machine learning frameworks such as MLaaS. These results will be achieved through the development of two new techniques for secure computation. The first technique will develop a multi-key homomorphic encryption scheme that is simultaneously a decentralized attribute-based encryption. The second technique will develop a new method for extending laconic protocols for oblivious-transfer. Successful development of these techniques will also directly advance the state-of-the-art in homomorphic encryption and secure computation protocols.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.