CAREER: A Trustworthy and Verifiable Software Backplane for the Cloud Edge

职业：值得信赖且可验证的云边缘软件背板

• 批准号: 2128725
• 负责人: Felix Xiaozhu Lin
• 金额: $ 47.97万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-12-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2128725&HistoricalAwards=false
• 关键词: CAREER; Trustworthy; Verifiable; Software; Backplane

Internet of things (IoT) produces a large influx of streaming data. The high cost and long latency in shipping raw IoT data necessitates edge processing. The objective of this project is to safeguard IoT data during edge processing. It seeks to establish a trusted data path on the edge. As the data flows through the path, the proposed edge system ensures 1) data confidentiality: IoT data is only accessible to trusted parties; 2) integrity: all the data having arrived at the edge is exactly manipulated according to the planned processing; 3) verifiability: data flows and computations on the edge are verifiable to a trusted cloud backend. The project's research thrusts will be tightly integrated with its education and outreach plan, educating students on trustworthy computing scenarios as well as stimulating the public's interests in science, technology, engineering, and math (STEM). There are plans to contribute fresh contents to curriculum; enhance student maker activities with in-situ IoT analytics; provide a summer course on IoT/Edge for local children; and, present relevant outreach at community IoT events. As IoT is revolutionizing modern society, the project aims toward more secure and efficient intelligence driven by IoT data.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.

物联网（IoT）产生大量流数据。运输原始物联网数据的高成本和长延迟需要边缘处理。该项目的目标是在边缘处理期间保护物联网数据。它试图在边缘建立可信的数据路径。当数据流经路径时，所提出的边缘系统确保1）数据机密性：物联网数据仅可由受信任方访问; 2）完整性：所有到达边缘的数据都根据计划的处理进行精确操作; 3）可验证性：边缘上的数据流和计算可由受信任的云后端验证。该项目的研究重点将与其教育和推广计划紧密结合，教育学生了解可信的计算场景，并激发公众对科学，技术，工程和数学（STEM）的兴趣。计划为课程提供新的内容;通过现场物联网分析加强学生创客活动;为当地儿童提供物联网/边缘暑期课程;并在社区物联网活动中提供相关推广。随着物联网对现代社会的变革，该项目旨在通过物联网数据驱动实现更安全、更高效的智能。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影响审查标准进行评估，被认为值得支持。

项目成果

Efficient NLP Model Finetuning via Multistage Data Filtering

• DOI: 10.24963/ijcai.2023/455
• 发表时间: 2022-07
• 作者: Ouyang Xu;S. Ansari;F. Lin;Yangfeng Ji

STI: Turbocharge NLP Inference at the Edge via Elastic Pipelining

• DOI: 10.1145/3575693.3575698
• 发表时间: 2022-07
• 期刊: Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2
• 作者: Liwei Guo;Wonkyo Choe;F. Lin

Video Analytics with Zero-streaming Cameras

• 发表时间: 2019-04
• 期刊: IEEE INFOCOM 2022 - IEEE Conference on Computer Communications
• 作者: Mengwei Xu;Tiantu Xu;Yunxin Liu;F. Lin

Safe and Practical GPU Computation in TrustZone

• DOI: 10.1145/3552326.3567483
• 发表时间: 2023-05
• 期刊: Proceedings of the Eighteenth European Conference on Computer Systems
• 作者: Heejin Park;F. Lin

Towards Out-of-core Neural Networks on Microcontrollers

• DOI: 10.1109/sec54971.2022.00008
• 发表时间: 2022-12
• 期刊: 2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)
• 作者: Hongyu Miao;F. Lin