The consumer electronics industry is witnessing a surge in Internet of Things (IoT) devices, ranging from mundane artifacts to complex biosensors connected across disparate networks. As the demand for IoT devices grows, the need for stronger authentication and access control mechanisms is greater than ever. Legacy authentication and access control mechanisms do not meet the growing needs of IoT. In particular, there is a dire need for a holistic authentication mechanism throughout the IoT device life-cycle, namely from the manufacturing to the retirement of the device. As a plausible solution, we present Authentication of Things (AoT), a suite of protocols that incorporate authentication and access control during the entire IoT device life span. Primarily, AoT relies on Identity-and Attribute-Based Cryptography to cryptographically enforce Attribute-Based Access Control (ABAC). Additionally, AoT facilitates secure (in terms of stronger authentication) wireless interoperability of new and guest devices in a seamless manner. To validate our solution, we have developed AoT for Android smartphones like the LG G4 and evaluated all the cryptographic primitives over more constrained devices like the Intel Edison and the Arduino Due. This included the implementation of an Attribute-Based Signature (ABS) scheme. Our results indicate AoT ranges from highly efficient on resource-rich devices to affordable on resource-constrained IoT-like devices. Typically, an ABS generation takes around 27 ms on the LG G4, 282 ms on the Intel Edison, and 1.5 s on the Arduino Due.
Graph neural networks (GNNs) have limited expressive power, failing to represent many graph classes correctly. While more expressive graph representation learning (GRL) alternatives can distinguish some of these classes, they are significantly harder to implement, may not scale well, and have not been shown to outperform well-tuned GNNs in real-world tasks. Thus, devising simple, scalable, and expressive GRL architectures that also achieve real-world improvements remains an open challenge. In this work, we show the extent to which graph reconstruction-reconstructing a graph from its subgraphs-can mitigate the theoretical and practical problems currently faced by GRL architectures. First, we leverage graph reconstruction to build two new classes of expressive graph representations. Secondly, we show how graph reconstruction boosts the expressive power of any GNN architecture while being a (provably) powerful inductive bias for invariances to vertex removals. Empirically, we show how reconstruction can boost GNN's expressive power-while maintaining its invariance to permutations of the vertices-by solving seven graph property tasks not solvable by the original GNN. Further, we demonstrate how it boosts state-of-the-art GNN's performance across nine real-world benchmark datasets.Preprint. Under review.
Existing Graph Neural Network (GNN) methods that learn inductive unsupervised graph representations focus on learning node and edge representations by predicting observed edges in the graph. Although such approaches have shown advances in downstream node classification tasks, they are ineffective in jointly representing larger k-node sets, k>2. We propose MHM-GNN, an inductive unsupervised graph representation approach that combines joint k-node representations with energy-based models (hypergraph Markov networks) and GNNs. To address the intractability of the loss that arises from this combination, we endow our optimization with a loss upper bound using a finite-sample unbiased Markov Chain Monte Carlo estimator. Our experiments show that the unsupervised joint k-node representations of MHM-GNN produce better unsupervised representations than existing approaches from the literature. * http://cottascience.github.io/ Preprint. Under review.
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