Computationally Sound Symbolic Security Reduction Analysis of Group Key Exchange Protocol using Bilinear Pairings

Canetti and Herzog have proposed a universally composable symbolic analysis (UCSA) of mutual authentication and key exchange protocols within universally composable security framework. It is fully automated and computationally sound symbolic analysis. Furthermore, Canetti and Gajek have analyzed Diffie-Hellman based key exchange protocols as an extension of their work. It deals with forward secrecy in case of fully adaptive party corruptions. However, their work only addresses two-party protocols that use public key encryptions, digital signatures and Diffie-Hellman exchange. We make the following contributions. First, we extend UCSA approach to analyze group key exchange protocols that use bilinear pairings exchange and digital signatures to resist insider attack under fully adaptive party corruptions with respect to forward secrecy. Specifically, we propose an formal algebra, and property of bilinear pairings in the execution of group key exchange protocol among arbitrary number of participants. This provides computationally sound and fully automated analysis. Second, we reduce the security of multiple group key exchange sessions among arbitrary number of participants to the security of a single group key exchange session among three participants. This improves the efficiency of security analysis

Coarse-to-Fine Contrastive Learning on Graphs

Inspired by the impressive success of contrastive learning (CL), a variety of graph augmentation strategies have been employed to learn node representations in a self-supervised manner. Existing methods construct the contrastive samples by adding perturbations to the graph structure or node attributes. Although impressive results are achieved, it is rather blind to the wealth of prior information assumed: with the increase of the perturbation degree applied on the original graph, 1) the similarity between the original graph and the generated augmented graph gradually decreases; 2) the discrimination between all nodes within each augmented view gradually increases. In this paper, we argue that both such prior information can be incorporated (differently) into the contrastive learning paradigm following our general ranking framework. In particular, we first interpret CL as a special case of learning to rank (L2R), which inspires us to leverage the ranking order among positive augmented views. Meanwhile, we introduce a self-ranking paradigm to ensure that the discriminative information among different nodes can be maintained and also be less altered to the perturbations of different degrees. Experiment results on various benchmark datasets verify the effectiveness of our algorithm compared with the supervised and unsupervised models

MNER-QG: An End-to-End MRC framework for Multimodal Named Entity Recognition with Query Grounding

Multimodal named entity recognition (MNER) is a critical step in information extraction, which aims to detect entity spans and classify them to corresponding entity types given a sentence-image pair. Existing methods either (1) obtain named entities with coarse-grained visual clues from attention mechanisms, or (2) first detect fine-grained visual regions with toolkits and then recognize named entities. However, they suffer from improper alignment between entity types and visual regions or error propagation in the two-stage manner, which finally imports irrelevant visual information into texts. In this paper, we propose a novel end-to-end framework named MNER-QG that can simultaneously perform MRC-based multimodal named entity recognition and query grounding. Specifically, with the assistance of queries, MNER-QG can provide prior knowledge of entity types and visual regions, and further enhance representations of both texts and images. To conduct the query grounding task, we provide manual annotations and weak supervisions that are obtained via training a highly flexible visual grounding model with transfer learning. We conduct extensive experiments on two public MNER datasets, Twitter2015 and Twitter2017. Experimental results show that MNER-QG outperforms the current state-of-the-art models on the MNER task, and also improves the query grounding performance.Comment: 13 pages, 6 figures, published to AAA

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The effect of postings information on searching behaviour An agent-based framework for Web query answering