CNS Core: Small: Privacy-Preserving On-Device Intelligence in the IoT Era

CNS 核心：小型：物联网时代保护隐私的设备上智能

• 批准号: 2151238
• 负责人: Xiaoyong Yuan
• 金额: $ 50万
• 依托单位: Michigan Technological University
• 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: 2022-07-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2151238&HistoricalAwards=false
• 关键词: CNS; Core; Small; Privacy; Preserving

On-device artificial intelligence (AI) is playing an increasingly important role in the Internet-of-Things (IoT) era. As a key enabler, model compression can accommodate state-of-the-art AI models within limited IoT resources without significantly compromising prediction performance. Given the connectivity among IoT devices, on-device intelligence is expected to not only serve individual IoT users more quickly and safely, but also support the broader IoT community. However, the growing IoT market makes IoT devices a primary target of adversaries. Meanwhile, recent research has shown severe privacy threats of AI models. When IoT meets AI, on-device intelligence is more likely to be subject to multiple new attack vectors, with significant potential impacts. This project consists of three research thrusts to develop privacy-preserving on-device intelligence in the IoT era: (i) exploring new vulnerabilities and the corresponding countermeasures for private on-device inference with model compression; (ii) establishing a private collaborative inference framework to protect intermediate on-device representations with high prediction performance; and (iii) investigating uncertainty-aware knowledge aggregation with lightweight, on-device obfuscation to protect collaborative training privacy. The proposed research will be rigorously evaluated via simulation tools, a large-scale testbed, and real-world deployment.The success of this project will bring tangible benefits to a range of domains in the IoT era, including healthcare, cyber-physical systems, transportation, and education. The proposed research will unlock the potential of intelligent IoT applications and services while preserving privacy, democratizing AI for a more diverse community. The programs, models, and testbeds developed in the project will be publicly released to boost community progress in this research area. Further, research findings will be integrated within existing courses, educational programs, and K-12 outreach activities to encourage students, especially underrepresented groups, to pursue STEM education and research.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.

设备上的人工智能（AI）在物联网（IoT）时代发挥着越来越重要的作用。作为一个关键的推动因素，模型压缩可以在有限的物联网资源内容纳最先进的AI模型，而不会显著影响预测性能。考虑到物联网设备之间的连接性，设备上的智能不仅可以更快、更安全地为个人物联网用户提供服务，还可以支持更广泛的物联网社区。然而，不断增长的物联网市场使物联网设备成为对手的主要目标。与此同时，最近的研究表明，AI模型存在严重的隐私威胁。当物联网与人工智能相遇时，设备上的智能更有可能受到多个新攻击向量的影响，并产生重大的潜在影响。该项目包括三个研究方向，以开发物联网时代的隐私保护设备上智能：（i）探索模型压缩的私有设备上推理的新漏洞和相应对策;（ii）建立一个私有协作推理框架，以保护具有高预测性能的中间设备上表示;（iii）建立一个私有协作推理框架，以保护具有高预测性能的中间设备上表示。以及（iii）研究具有轻量级设备上混淆的不确定性感知知识聚合，以保护协作训练隐私。该项目的成功将为物联网时代的医疗、网络物理系统、交通和教育等一系列领域带来实实在在的贝内。拟议中的研究将释放智能物联网应用和服务的潜力，同时保护隐私，使AI民主化，为更多样化的社区服务。该项目中开发的程序，模型和测试平台将公开发布，以促进该研究领域的社区进展。此外，研究成果将被整合到现有的课程，教育计划和K-12推广活动中，以鼓励学生，特别是代表性不足的群体，追求STEM教育和研究。该奖项反映了NSF的法定使命，并被认为值得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来支持。