CRII: SHF: RUI: Custom Hardware Accelerators for Privacy-Preserving Image Processing

CRII：SHF：RUI：用于保护隐私的图像处理的定制硬件加速器

基本信息

批准号：
2105373
负责人：
Sunwoong Kim
金额：
$ 17.44万
依托单位：
University of Washington
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：
2021-09-01 至 2023-11-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2105373&HistoricalAwards=false
关键词：
CRII SHF RUI Custom Hardware

项目摘要

Homomorphic encryption is an important cryptographic technique that allows direct computation on encrypted data without decryption. The result of the computation is entirely encrypted, and only the data owner can decrypt it using a private key. As a result, data analysts such as cloud-service providers cannot view any private or sensitive information from the data. Promising applications of homomorphic encryption include financial and medical data analytics and privacy-preserving machine learning, and homomorphic encryption can be also used for more diverse applications such as genomics, national security/critical infrastructures, and elections. Several emerging applications makes homomorphic encryption increasingly essential. However, homomorphic encryption suffers from slow processing speed, which renders it impractical for many critical applications. In addition, it only supports addition and/or multiplication for encrypted data, which limits the scope of its applications. This project will address these issues using custom hardware accelerators and a numerical approach that approximates several arithmetic and logical operations using addition and multiplication. This project will broaden the scope of practical homomorphic-encryption-based applications and provide opportunities for students to gain experience in a variety of areas such as digital system design, software programming, and cryptography.This project will focus on homomorphic-encryption-based image-processing applications. In typical homomorphic encryption-based systems, a user encrypts private data on a local device and sends them to a cloud server where a homomorphic encryption-based application is performed. However, the inclusion of noise or brightness distortion in the original images can negatively affect the results of the application. Therefore, one of the goals of this project is to develop custom hardware accelerators for homomorphic-encryption-based noise cancelling and contrast enhancement using field-programmable gate arrays. They require division and min/max functions for encrypted data, and these operations are being numerically implemented. Another problem arises when data analysts convey homomorphic-encryption-driven analysis results to a user. They can only refer to specific data points associated with a section of the image, but not the original image itself as it is encrypted. This can be problematic on the user's side as they may not understand which part of the image the data analyst is referring to. Thus, another goal is to develop a custom hardware accelerator for an image-thresholding technique for highlighting regions of interest. It requires comparison operations for encrypted data, and the numerical approach is being used again. This project aims at reducing the computational runtime overhead by one order to two orders of magnitude compared to software implementations.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.

同态加密是一种重要的加密技术，可以直接计算加密数据而无需解密。计算的结果完全加密，只有数据所有者才能使用私钥解密。结果，诸如云服务提供商之类的数据分析师无法从数据中查看任何私人或敏感信息。同态加密的有前途的应用包括财务和医疗数据分析以及保护隐私的机器学习，以及同型加密也可以用于更多样化的应用，例如基因组学，国家安全/关键基础架构和选举。几种新兴应用程序使同态加密越来越重要。但是，同态加密的处理速度缓慢，这对于许多关键应用而言是不切实际的。此外，它仅支持加密数据的添加和/或乘法，这限制了其应用程序的范围。该项目将使用自定义硬件加速器和一种数值方法来解决这些问题，该方法使用添加和乘法近似几个算术和逻辑操作。该项目将扩大基于同质加入的应用程序的范围，并为学生提供在数字系统设计，软件编程和密码学等各个领域中获得经验的机会。此项目将重点介绍基于同型默认的图像处理应用程序。在基于典型的同态加密系统中，用户在本地设备上加密私有数据，并将其发送到执行基于同质加密应用程序的云服务器。但是，在原始图像中包含噪声或亮度失真会对应用程序的结果产生负面影响。因此，该项目的目标之一是开发自定义硬件加速器，用于使用现场可编程的门阵列来基于同型加密的降噪和对比度增强。它们需要分区和最小/最大功能来加密数据，并且这些操作正在数值上实施。当数据分析师向用户传达同型加密驱动的分析结果时，就会出现另一个问题。他们只能参考与图像一部分相关的特定数据点，而不是在加密时不参考原始图像本身。这在用户方面可能是有问题的，因为他们可能不了解数据分析师所指的图像的哪一部分。因此，另一个目标是为图像阈值技术开发一种自定义硬件加速器，以突出关注的区域。它需要对加密数据进行比较操作，并且正在再次使用数值方法。与软件实施相比，该项目旨在将计算运行时开销降低到两个数量级。该奖项反映了NSF的法定任务，并且使用基金会的智力优点和更广泛的影响审查标准，认为值得通过评估值得支持。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

HEMTH: Small Depth Multilevel Thresholding for a Homomorphically Encrypted Image

DOI：
10.1109/mmsp55362.2022.9949585
发表时间：
2022-09
期刊：
2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP)
影响因子：
0
作者：
Paul Nam;Justin Shyi;Sunwoong Kim
通讯作者：
Paul Nam;Justin Shyi;Sunwoong Kim

HEKWS: Privacy-Preserving Convolutional Neural Network-based Keyword Spotting with a Ciphertext Packing Technique

DOI：
10.1109/mmsp55362.2022.9949982
发表时间：
2022-09
期刊：
2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP)
影响因子：
0
作者：
Daniel L. Elworth;Sunwoong Kim
通讯作者：
Daniel L. Elworth;Sunwoong Kim

HEBGS: Homomorphic Encryption-based Background Subtraction Using a Fast-Converging Numerical Method