CNS Core: Small: Enabling Privacy-Preserving Routing-on-Context in IoT

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Over the past 50 years, Internet has evolved from initially a data network connecting only computers to an Internet of Things (IoT) - a global cyber-physical network that connects not only computers but also things in the physical world. Compared with computers, the connected things are closely coupled with their physical environment (a.k.a. the context) through sensing. As a result, IoT applications are typically context-oriented, i.e., being aware of and sensitive to the physical circumstances that form the interest of the application, while conventional computer-based Internet applications are context agnostic. Despite the distinct disparities between connected things and computers and the fact that Internet traffic is shifting from computer communication to context-oriented IoT applications, surprisingly, today's Internet routing principle has little change than the long-standing connectivity-based routing, characterized as connecting a specific source node with a specific destination node. Context-oriented IoT applications do not perform well under this traditional routing model, because what these applications are essentially targeting on is the application context, rather than a specific destination node. To better support IoT in the future Internet, this project seeks to establish a new efficient and privacy-preserving routing technique that is based on the targeted application context but does not disclose this information. This project will provide the urgently-needed solution to protect the privacy of tens of millions of IoT users while supporting their efficient usage of IoT, thus making a deep impact on the nation's economy and social well-being. This project will also carry out a comprehensive education plan to broaden its impact, with an emphasis on underrepresented and minority groups. This project will advance the state of the art in privacy-preserving context-based routing. The PI will develop new tools and techniques for routing in the encrypted space, whereby geographic routes are computed and represented directly based upon encrypted location information. In particular, two novel techniques will be developed: (i) a novel space-filling-curve-based method for encrypting the space; (ii) a Kademlia-tree-based routing technique to achieve efficient and privacy-preserving geographic routing in the encrypted space. The project will also establish the efficiency-privacy tradeoff for the proposed methods under various special conditions such as mobility and different attack models.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.