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: 2211215
• 负责人: Na Gong
• 金额: $ 30万
• 依托单位: University of South Alabama
• 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 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2211215&HistoricalAwards=false
• 关键词: 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.

差分隐私（DP）已被广泛认为是保护数据隐私的事实上的技术，尽管对差分隐私的研究工作已有十年之久，但仍然存在一个被很大程度上忽视的关键研究问题，即所有现有的差分隐私研究都是有基础的。假设软件可以轻松、忠实地从概率分布中采样并添加噪声，但是，这一假设不断受到有关其隐私侵犯的最新发现以及可能缺乏高水平的低端设备日益增长的隐私保护需求的挑战。因此，这个级别的软件库。该项目的创新研究角度是直接在现代电子设备中普遍存在的嵌入式存储器上实现DP机制，在技术方面，所开发的创新具有以下优点（1）它将主机设备从专用软件中解放出来，并实现了这一愿景。 “隐私设计”；（2）它同时提高了多种系统性能，如电源效率、隐私和芯片开销；（3）所开发的技术是原始的、通用的，并且可扩展至每个电子设备。该项目还将创造深远的影响。具体来说，所开发的技术将对众多敏感应用（例如监控和传感）和关键基础设施（例如物联网设备）产生变革。它可能会增加美国芯片供应商的数量。通过在芯片中添加隐私保护功能来增加收入和竞争功能，保护纳税人和企业的敏感数据，维护国家安全，并帮助美国在网络安全方面击败全球竞争对手。此外，该项目还将帮助 PI 进行更新。现有课程，让学生（优先考虑女性或/和第一代大学生）参与研究培训，并通过现有的成功项目（例如位于阿拉巴马州莫比尔市的 NSF 资助的 RET 站点）促进社区外展。该奖项反映了 NSF 的法定使命通过使用基金会的智力价值和更广泛的影响审查标准进行评估，并被认为值得支持。

项目成果

Approximate Memory for Low-Power Video Applications

低功耗视频应用的近似内存

• DOI: 10.1109/access.2023.3283409
• 发表时间: 2023
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Das, H.;Haidous, A. A.;Smith, S. C.;Gong, N.
• 通讯作者: Gong, N.