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

SaTC：核心：小型：加速实时安全应用程序的隐私保护深度学习

基本信息

批准号：
2104264
负责人：
Viktor Prasanna
金额：
$ 49.95万
依托单位：
University of Southern California
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-01 至 2024-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2104264&HistoricalAwards=false
关键词：
SaTC CORE Small Accelerating Privacy

项目摘要

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.

目前，为了从数据中获取洞察力，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。为了实现数据安全性，数据由所有者加密并发送到云服务器，在那里解密以执行分析。对于隐私敏感的应用，如医疗保健、金融等，这导致数据安全问题，因为云上的解密数据可能被恶意行为者窥探。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进行机器学习（ML）分析，而无需解码，从而实现端到端的隐私。拟议项目将开发针对现场可编程门阵列（FPGA）的优化，以解决共享对象的并行访问冲突，不规则内存访问，低数据重用等挑战，这在许多应用领域中是普遍的。此外，用于隐私保护深度神经网络（DNN）的关键内核（例如数论变换（NTT）、旋转、乘法等）的参数化FPGA知识产权（IP）核，该项目将开发的新技术将允许应用程序开发人员轻松实现各种隐私保护机器学习/深度学习模型。此外，所提出的加速技术适用于依赖于基于后量子晶格的密码学的应用。这项工作的更广泛影响是有效地使用新兴的数据中心和云平台来加速基于同态加密（HE）的DNN，以实现实时安全应用。该项目的成功完成将显著提高隐私敏感应用程序的功能，使其能够以可信和安全的方式利用公共云。该项目将通过南加州大学的各种课程，识别并向代表性不足和服务不足的学生介绍STEM（科学，技术，工程，数学）。拟议的研究还将构成适合纳入研究生和本科生课程的材料。项目中开发的所有软件将发布在github上：https://github.com/pgroupATusc。软件版本将在资助结束后保留不少于3年的时间。该奖项反映了NSF的法定使命，并且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。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

FPGA Acceleration of Fully Homomorphic Encryption over the Torus

DOI：
10.1109/hpec55821.2022.9926381
发表时间：
2022-09
期刊：
2022 IEEE High Performance Extreme Computing Conference (HPEC)
影响因子：
0
作者：
Tian Ye;R. Kannan;V. Prasanna
通讯作者：
Tian Ye;R. Kannan;V. Prasanna

HyScale-GNN: A Scalable Hybrid GNN Training System on Single-Node Heterogeneous Architecture

DOI：
10.1109/ipdps54959.2023.00062
发表时间：
2023-03
期刊：
2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
影响因子：
0
作者：
Yi-Chien Lin;V. Prasanna
通讯作者：
Yi-Chien Lin;V. Prasanna

NTTGen: a framework for generating low latency NTT implementations on FPGA