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In this paper, we study the properties of path metrics of an entanglement path for a given entanglement swapping order of the path. We show how to efficiently compute the path metrics of an entanglement path for any given swapping order. We show that different entanglement swapping orders for the same path can lead to different expected throughputs. A key finding is that the binary operator corresponding to entanglement swapping along a path is not associative. We further show that the problem of computing an s-t path with maximum expected throughput under any entanglement swapping order does not have the subpath optimality property, which is a key property most path finding algorithms such as Dijkstra’s algorithm rely on. We use extensive simulations to validate our theoretical findings. 

Recent advances in cyber-physical systems, artificial intelligence, and cloud computing have driven the wide deployments of Internet-of-things (IoT) in smart homes. As IoT devices often directly interact with the users and environments, this paper studies if and how we could explore the collective insights from multiple heterogeneous IoT devices to infer user activities for home safety monitoring and assisted living. Specifically, we develop a new system, namely IoTMosaic, to first profile diverse user activities with distinct IoT device event sequences, which are extracted from smart home network traffic based on their TCP/IP data packet signatures. Given the challenges of missing and out-of-order IoT device events due to device malfunctions or varying network and system latencies, IoTMosaic further develops simple yet effective approximate matching algorithms to identify user activities from real-world IoT network traffic. Our experimental results on thousands of user activities in the smart home environment over two months show that our proposed algorithms can infer different user activities from IoT network traffic in smart homes with the overall accuracy, precision, and recall of 0.99, 0.99, and 1.00, respectively. 

Reputation systems, designed to remedy the lack of information quality and assess credibility of information sources, have become an indispensable component of many online systems. A typical reputation system works by tracking all information originating from a source, and the feedback to the information with its attribution to the source. The tracking of information and the feedback, though essential, could violate the privacy of users who provide the information and/or the feedback, which could both cause harm to the users' online well-being, and discourage them from participation. Anonymous reputation systems have been designed to protect user privacy by ensuring anonymity of the users. Yet, current anonymous reputation systems suffer from several limitations, including but not limited to a)lack of support for core functionalities such as feedback update, b) lack of protocol efficiency for practical deployment, and c) reliance on a fully trusted authority. This paper proposes EARS, an anonymous reputation system that ensures user anonymity while supporting all core functionalities (including feedback update) of a reputation system both efficiently and practically, and without the need of a fully trusted central authority. We present security analysis of EARS against multiple types of attacks that could potentially violate user anonymity, such as feedback duplication, bad mouthing, and ballot stuffing. We also present evaluation of the efficiency and scalability of our system based on implementations.