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SaTC: CORE: Medium: Collaborative: Privacy-Aware Trustworthy Control as a Service for the Internet of Things (IoT)

SaTC：核心：媒介：协作：物联网 (IoT) 的隐私意识可信控制即服务

基本信息

批准号：
1705135
负责人：
Mani Srivastava
金额：
$ 85万
依托单位：
University of California-Los Angeles
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1705135&HistoricalAwards=false
关键词：
SaTC CORE Medium Collaborative Privacy

项目摘要

The Internet of Things (IoT) includes a variety of devices such as smart appliances, cars, and other physical systems that are deeply embedded in our everyday lives and that are at risk from new kinds of threats to security and privacy from hackers or state actors. In IoT systems, sensors are used to probe the physical state of the system (e.g., temperature in a building or rotational speed of a wheel of a car) and then software control systems use algorithms to determine appropriate adjustments to the system (e.g., run the air conditioning for 5 minutes or apply the brakes). The project is focused on protecting those control systems and algorithms to ensure security and privacy for users.The research addresses trustworthy and privacy-aware control architectures for IoT through mechanisms drawn from control, cryptography, software, and hardware. These include: (i) A framework for formally reasoning about safety and privacy properties of control software in conjunction with dynamical models of the physical world and associated sensing and actuation channels; (ii) Lightweight domain-specific mechanisms, for policing flow of information through software applications, while leveraging the semantics of machine learning and control algorithms, physics of the system, and numerical properties; (iii) Enforcing desired safety and information leakage properties via a combination of principled sensor data perturbation, control algorithms optimized for efficient computation over encrypted data, and a hardware-supported trusted computing base tailored to protecting sensed data and control algorithm parameters; (iv) A resilient control and timing infrastructure that protects against attacks on timing information through a hybrid use of edge and cloud resources and physical models. The success of the mechanisms is being assessed on experimental testbeds for smart home, industrial automation and smart vehicles, but have broader applicability to many other IoT applications. The project team is also creating a new graduate class on IoT security and developing educational material on IoT security for high-schoolers through the Los Angeles Computing Circle initiative at the University of California - Los Angeles.

物联网（IoT）包括各种各样的设备，如智能家电、汽车和其他物理系统，这些设备深深嵌入我们的日常生活中，面临着黑客或国家行为者对安全和隐私的新型威胁。在物联网系统中，传感器用于探测系统的物理状态（例如，建筑物的温度或汽车车轮的转速），然后软件控制系统使用算法来确定对系统的适当调整（例如，运行空调5分钟或应用刹车）。该项目的重点是保护这些控制系统和算法，以确保用户的安全和隐私。该研究通过从控制、密码学、软件和硬件中提取的机制，解决了物联网的可信和隐私感知控制架构。这些包括：(i)结合物理世界的动态模型和相关的传感和驱动通道，对控制软件的安全和隐私属性进行正式推理的框架；（ii）轻量级领域特定机制，用于通过软件应用程序监管信息流，同时利用机器学习和控制算法的语义、系统的物理特性和数值属性；（iii）通过以下措施加强所需的安全和信息泄漏特性：有原则的传感器数据扰动、为对加密数据进行有效计算而优化的控制算法，以及为保护感测数据和控制算法参数而量身定制的硬件支持的可信计算基础；（四）弹性控制和定时基础设施，通过混合使用边缘和云资源以及物理模型，防止定时信息受到攻击。这些机制的成功正在智能家居、工业自动化和智能汽车的实验测试平台上进行评估，但对许多其他物联网应用具有更广泛的适用性。项目团队还创建了一个新的物联网安全研究生班，并通过加州大学洛杉矶分校的洛杉矶计算圈计划为高中生开发物联网安全教育材料。

项目成果

期刊论文数量（31）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Generating Natural Language Adversarial Examples

DOI：
10.18653/v1/d18-1316
发表时间：
2018-04
期刊：
ArXiv
影响因子：
0
作者：
M. Alzantot;Yash Sharma;Ahmed Elgohary;Bo-Jhang Ho;M. Srivastava;Kai-Wei Chang
通讯作者：
M. Alzantot;Yash Sharma;Ahmed Elgohary;Bo-Jhang Ho;M. Srivastava;Kai-Wei Chang

TinyNS: Platform-aware Neurosymbolic Auto Tiny Machine Learning

DOI：
10.1145/3603171
发表时间：
2024-05-01
期刊：
ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS
影响因子：
2
作者：
Saha，Swapnil Sayan;Sandha，Sandeep Singh;Srivastava，Mani
通讯作者：
Srivastava，Mani

Decentralized Resilient State-Tracking

去中心化的弹性状态跟踪

DOI：
10.1109/cdc45484.2021.9683313
发表时间：
2021
期刊：
2021 60th IEEE Conference on Decision and Control (CDC
影响因子：
0
作者：
Mao, Yanwen;Tabuada, Paulo
通讯作者：
Tabuada, Paulo

Hard-Label Black-Box Adversarial Attack on Deep Electrocardiogram Classifier

对深度心电图分类器的硬标签黑盒对抗攻击

DOI：
10.1145/3417312.3431827
发表时间：
2020
期刊：
SecICPS '20: Proceedings of the 1st ACM International Workshop on Security and Safety for Intelligent Cyber-Physical Systems
影响因子：
0
作者：
Lam, Jonathan;Quan, Pengrui;Xu, Jiamin;Jeyakumar, Jeya Vikranth;Srivastava, Mani
通讯作者：
Srivastava, Mani

SecDeep: Secure and Performant On-device Deep Learning Inference Framework for Mobile and IoT Devices

DOI：
10.1145/3450268.3453524
发表时间：
2021-05
期刊：
Proceedings of the International Conference on Internet-of-Things Design and Implementation
影响因子：
0
作者：
Renju Liu;L. Garcia;Zaoxing Liu;Botong Ou;M. Srivastava
通讯作者：
Renju Liu;L. Garcia;Zaoxing Liu;Botong Ou;M. Srivastava