Collaborative Research: CNS Core: Small: Privacy by Memory Design

合作研究：CNS 核心：小型：内存设计的隐私

• 批准号: 2211214
• 负责人: Jianqing Liu
• 金额: $ 30万
• 依托单位: University of Alabama in Huntsville
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2023-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2211214&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Small; Collaborative; Research; CNS; Core; Small

Differential privacy (DP) has been widely accepted as the de facto technique for protecting data privacy. Despite the decade-long research efforts on DP, there still exists a critical research problem that has been largely overlooked, that is all existing DP studies are grounded on the hypothesis that software can easily and faithfully sample and add noises from a probability distribution. However, this hypothesis is being constantly challenged by recent findings about its privacy violation and by the growing demand of privacy protection in low-end devices that may lack high-level software libraries. Hence, this project's innovative research angle is to realize DP mechanisms directly on embedded memories, which are ubiquitous in modern electronic devices. On the technical front, the developed innovation has the following merits. (1) It frees host devices from dedicated software and accomplishes the vision of "privacy by design"; (2) It concurrently improves manifold system performance such as power efficiency, privacy, and chip overhead; (3) The developed technique is primitive, generic, and scalable to every electronic device.This project will also create profound impact on our society, economy, and workforce development. Specifically, the developed technique will be transformative to numerous sensitive applications (e.g., surveillance and sensing) and critical infrastructures (e.g., Internet of Things devices). It will potentially increase the U.S. chip vendors' revenue and competitiveness by adding privacy-preserving functionality to their chips, protect taxpayers' and enterprises' sensitive data, safeguard national security, and help the U.S. out-compete global competitors in cybersecurity. Moreover, this project will help PIs to update existing curricula, engage students – with priority to female or/and first-generation college students – for research training, and promote community outreach through existing successful programs such as the NSF-funded RET site in Mobile, Alabama.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.

差异隐私（DP）已被广泛接受为保护数据隐私的事实上的技术。尽管对DP进行了长达十年的研究工作，但仍然存在一个关键的研究问题，这些问题在很大程度上被忽略了，这是所有现有的DP研究都基于以下假设：软件可以轻松而忠实地进行采样和添加概率分布中的声音。但是，最近对可能缺乏高级软件库的低端设备中对隐私保护的需求的最新发现以及对隐私保护的日益增长的需求不断受到挑战。因此，该项目的创新研究角度是直接在嵌入式记忆上实现DP机制，这些记忆在现代电子设备中无处不在。在技​​术方面，开发的创新具有以下优点。 （1）它从专用软件中释放主机设备，并实现“设计隐私”的愿景； （2）同时改善了多种系统性能，例如功率效率，隐私和芯片开销； （3）开发的技术对每个电子设备都是原始的，通用的和可扩展的。本项目还将对我们的社会，经济和劳动力发展产生深远的影响。特别是，开发的技术将对众多敏感应用（例如监视和敏感性）和关键基础设施（例如，Internet设备设备）进行变革。它将通过在其芯片上增加隐私权功能，保护纳税人和企业的敏感数据，保障国家安全，并帮助美国在网络安全中超越全球竞争对手。 Moreover, this project will help PIs to update existing curricula, engage students – with priority to female or/and first-generation college students – for research training, and promote community outreach through existing successful programs such as the NSF-funded RET site in Mobile, Alabama.This award reflects NSF's statutory mission and has been deemed honestly of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.