SaTC: CORE: Small: Accelerating Privacy Preserving Deep Learning for Real-time Secure Applications

目前，为了从数据中获取见解，所有者需要将它们发送到云服务器，以执行复杂的基于机器学习的分析。为了启用数据安全性，数据由所有者加密并发送到云服务器，在云服务器上对其进行解密以执行分析。对于隐私敏感的应用程序，如医疗保健、金融等，这会导致数据安全问题，因为云上的解密数据可能会被恶意行为者窥探。为了解决这一问题，该提案将开发技术，以有效地对加密数据执行机器学习（ML）分析，而无需解码，从而实现端到端隐私。提出的项目将针对现场可编程门阵列（fpga）进行优化，以解决许多应用领域普遍存在的挑战，如并行访问共享对象时的冲突、不规则内存访问、低数据重用等。此外，将在该项目中开发的用于保护隐私的深度神经网络（dnn）关键内核的参数化FPGA知识产权（IP）内核，如数论变换（NTT）、旋转、乘法等，将允许应用程序开发人员轻松实现各种保护隐私的机器学习/深度学习模型。此外，所提出的加速技术适用于依赖后量子点阵加密的应用。这项工作的更广泛的影响是有效地利用新兴的数据中心和云平台来加速基于同态加密（HE）的dnn用于实时安全应用。该项目的成功完成将使隐私敏感应用程序能够以可信和安全的方式利用公共云，从而显著提高其功能。该项目将通过南加州大学的各种项目，确定并让代表性不足和服务不足的学生接触STEM（科学、技术、工程、数学）。拟议的研究也将构成适合纳入研究生和本科生课程的材料。该项目开发的所有软件将发布在github上：https://github.com/pgroupATusc。在拨款结束后，软件版本的维修期不少于3年。该奖项反映了美国国家科学基金会的法定使命，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。

Currently, to draw insights from data, the owner needs to send them to a cloud server to perform complex Machine Learning based analytics. To enable data security, the data is encrypted by the owner and sent to the cloud server where it is decrypted to perform analytics. For privacy sensitive applications such as healthcare, finance, etc., this leads to data security concerns as the decrypted data on the cloud may be snooped by malicious actors. To address this concern, this proposal will develop techniques to efficiently perform Machine Learning (ML) analytics on encrypted data, without a need for decoding, thereby enabling end-to-end privacy.The proposed project will develop optimizations targeting Field Programmable Gate Arrays (FPGAs) to address the challenges such as conflicts in parallel access to shared objects, irregular memory accesses, low data reuse, etc., which are prevalent in many application domains. Moreover, the parameterized FPGA Intellectual Property (IP) cores for the key kernels of privacy preserving Deep Neural Networks (DNNs) such as Number Theoretic Transform (NTT), rotation, multiplication, etc., that will be developed in the project will allow application developers to easily implement a wide variety of privacy preserving Machine Learning/Deep Learning models. Additionally, the proposed acceleration techniques are applicable to applications which rely on post-quantum lattice based cryptography.The broader impact of this work is in efficient use of emerging data center and cloud platforms for accelerating Homomorphic Encryption (HE) based DNNs for real-time secure applications. Successful completion of this project will lead to a significant increase in the capabilities of privacy sensitive applications by enabling them to utilize public clouds in a trusted and secure manner. The project will identify and expose underrepresented and underserved students to STEM (Science, Technology, Engineering, Mathematics) through various programs at the University of Southern California. The proposed research will also constitute materials appropriate for inclusion in graduate and undergraduate courses.All software developed in the project will be posted on github at: https://github.com/pgroupATusc. Software releases will be maintained for a period of not less than 3 years after the conclusion of the grant.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.