WiFiUS: Collaborative Research: Secure Inference in the Internet of Things

预计到2022年，下一代通信网络将提供连接多达1万亿个传感器、产品、机器和设备的物联网（IoT）。本研究解决了这些重要的新兴网络的安全问题。由于典型物联网终端的能力有限，预期的实时物联网应用的严格延迟要求，以及无线信道的广播性质，本项目研究物理层和低复杂度的加密方法，以防止窃听者获取物联网数据。物联网终端将经常通过短消息进行通信，这需要超越现有的基于无限长消息的理论。由于推理在物联网系统中起着核心作用，因此本研究侧重于基于推理的指标。该项目使学生能够在三所参与的大学接受培训，在主要会议上组织讲习班，并编制课程材料。在项目团队成员最近工作的基础上，该团队正在开发基本性能指标的边界和近似，以便理解和设计基于短包通信的推理网络。为了深入了解大规模推理网络，该团队正在研究推理性能的缩放定律，通过改变合法用户数量与窃听者数量的比例来实现最佳缩放行为。采用非二进制量化、符号翻转、信道状态信息和相关方法的新型实用低复杂度加密方法正在开发中，预计将比现有的物联网数据保护方法提供显着优势。补充已开发方法的分布式推理和学习方法也在开发中。针对物联网中分布式传感器的快速自举方法和低复杂度的估计、决策和分类方法也被提出。

The next generation of communication networks is predicted to provide an Internet of Things (IoT) interconnecting up to 1 trillion sensors, products, machines, and devices by 2022. This research addresses the critical problem of security in these important emerging networks. Due to the limited capability of typical IoT terminals, the strict delay requirements of anticipated real-time IoT applications, and the broadcast nature of wireless channels, this project investigates physical layer and low complexity cryptographic methods to prevent eavesdroppers from gaining access to IoT data. IoT terminals will often communication via short messages, requiring new theory beyond the existing theory based on infinitely long messages. Since inference takes a central role in IoT systems, this research focuses on inference-based metrics. This project enables student training at the three participating universities, organization of workshops at major conferences, and development of course materials.Building on very recent work by the members of the project team, the team is developing bounds and approximations to fundamental performance indices in order to understand and design inferential networks based on short packet communications. To gain insight into large-scale inference networks, the team is investigating scaling laws for inferential performance which achieve optimum scaling behavior as one varies the ratio of the number of legitimate users to the number of eavesdroppers. New practical low-complexity cryptographic methods employing non-binary quantization, symbol flipping, channel state information and related approaches are under development and are expected provide significant advantages over existing methods of protecting IoT data. Distributed inference and learning methods that complement the developed approaches are also under development. Fast bootstrapping methods and low-complexity estimation, decision making and classification methods for distributed sensors in IoT are being derived as well.