Collaborative Research: IMR: MM-1C: Privacy-preserving IoT Analytics and Behavior Prediction on Network Edge

合作研究：IMR：MM-1C：网络边缘上的隐私保护物联网分析和行为预测

• 批准号: 2219867
• 负责人: Yuxing Huang
• 金额: $ 29.99万
• 依托单位: New York 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: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2219867&HistoricalAwards=false
• 关键词: Collaborative; Research; IMR; MM; 1C

As the Internet of Things (IoT) products gain popularity on home networks, they disproportionately overburden home networks from a resource, security, and privacy point of view. Despite recent efforts to characterize IoT traffic, there is a lack of annotated datasets. Moreover, existing annotated traffic typically covers a handful of devices in a lab setting. Therefore, it is unclear if the results generalize to a large-scale real-world setting. Similarly, when it comes to developing privacy-preserving IoT analytics, existing approaches rely on simulated data or datasets that are not representative of real-world IoT traffic. To this end, this project explores new Internet measurement methodologies and privacy-preserving analytics for IoT devices. The project's broader significance and importance are to help network providers and consumers better manage their networks in terms of quality of service, security and privacy.This project first focuses on developing a new infrastructure to automatically collect and annotate IoT traffic by programmatically triggering different functionalities on IoT devices via companion apps and hidden APIs. To complement the annotation process, the project enables real-world participants to annotate the network traffic generated by their smart home IoT devices. Next, the project builds privacy-preserving machine learning models to predict traffic volume, traffic type, and anomalous device behavior even in the presence of imbalanced training data across the different tasks.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.

随着物联网 (IoT) 产品在家庭网络中越来越受欢迎，从资源、安全和隐私的角度来看，它们给家庭网络带来了不成比例的负担。尽管最近努力描述物联网流量的特征，但仍缺乏带注释的数据集。此外，现有的带注释的流量通常覆盖实验室环境中的少数设备。因此，尚不清楚结果是否可以推广到大规模的现实世界环境。同样，在开发保护隐私的物联网分析时，现有方法依赖于不代表现实世界物联网流量的模拟数据或数据集。为此，该项目探索了新的互联网测量方法和物联网设备的隐私保护分析。该项目更广泛的意义和重要性是帮助网络提供商和消费者在服务质量、安全和隐私方面更好地管理其网络。该项目首先侧重于开发新的基础设施，通过以编程方式触发不同的功能来自动收集和注释物联网流量。通过配套应用程序和隐藏 API 的 IoT 设备。为了补充注释过程，该项目使现实世界的参与者能够注释其智能家居物联网设备生成的网络流量。接下来，该项目构建了保护隐私的机器学习模型，即使在不同任务之间存在不平衡的训练数据的情况下，也可以预测流量、流量类型和异常设备行为。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。

项目成果

Augmented Reality's Potential for Identifying and Mitigating Home Privacy Leaks

增强现实在识别和减少家庭隐私泄露方面的潜力

• DOI: 10.14722/usec.2023.238930
• 发表时间: 2023-01-27
• 期刊: ArXiv
• 作者: Stefany Cruz;Logan Danek;Shinan Liu;Christopher Kraemer;Zixin Wang;N. Feamster;D. Huang;Yaxing Yao;Josiah D. Hester
• 通讯作者: Josiah D. Hester