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

差分隐私（DP）已被广泛接受为事实上的保护数据隐私的技术。尽管对DP的研究已经进行了十年之久，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DP研究都是基于这样的假设，即软件可以轻松而忠实地从概率分布中采样和添加噪声。然而，这一假设不断受到最近关于其隐私侵犯的调查结果的挑战，以及可能缺乏高级软件库的低端设备中日益增长的隐私保护需求。因此，本计画的创新性研究角度是直接在嵌入式记忆体上实作动态规划机制，这在现代电子装置中是普遍存在的。在技术方面，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，实现了“隐私设计”的愿景;（2）同时改善了多方面的系统性能，如功率效率，隐私和芯片开销;（3）开发的技术是原始的，通用的，可扩展到每一个电子设备。具体而言，所开发的技术将对许多敏感应用（例如，监视和传感）和关键基础设施（例如，物联网设备）。它将通过在芯片中增加隐私保护功能，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超越全球竞争对手，从而增加美国芯片供应商的收入和竞争力。此外，该项目将帮助PI更新现有课程，吸引学生（优先考虑女性或/和第一代大学生）进行研究培训，并通过现有的成功项目（例如NSF资助的RET站点）促进社区外展。在移动的该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的评估被认为值得支持。影响审查标准。

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.