Password authenticated key exchange (PAKE) allows two parties with a shared password to establish a session key. In order to provide secure and private communication between devices in an Internet of Things (IoT) environment, the PAKE protocol is considered one of the most common and promising security methods. However, many existing PAKE proposals still face challenges in security and efficiency. First, both the terminals of participants may suffer from the compromise attack and precomputation attack, which will lead to the leakage of password. Second, the majority of the existing schemes cannot guarantee participants’ identity privacy. Third, most PAKE protocols are not suitable for IoT devices with limited computing capability because of a large number of exponential and pairing operations. To address these issues, we propose a strong symmetric PAKE protocol for IoT devices, which only requires 3 exponentiations per party. The proposed scheme can protect both parties from compromise and precomputation attacks, in which the password file relies on the identity and random salt. What’s more, our protocol guarantees the identity privacy, that is, the transmitted record and password file will not reveal the identity information. We further present a new security model for our protocol, and prove that the proposed scheme is secure under this model. Finally, we show the practicality of our PAKE via experiments and efficiency analysis.

Cross-modal clustering aims to cluster the high-similar cross-modal data into one group while separating the dissimilar data. Despite the promising cross-modal methods have developed in recent years, existing state-of-the-arts cannot effectively capture the correlations between cross-modal data when encountering with incomplete cross-modal data, which can gravely degrade the clustering performance. To well tackle the above scenario, we propose a novel incomplete cross-modal clustering method that integrates canonical correlation analysis and exclusive representation, named incomplete Cross-modal Subspace Clustering (i.e., iCmSC). To learn a consistent subspace representation among incomplete cross-modal data, we maximize the intrinsic correlations among different modalities by deep canonical correlation analysis (DCCA), while an exclusive self-expression layer is proposed after the output layers of DCCA. We exploit a ℓ1,2-norm regularization in the learned subspace to make the learned representation more discriminative, which makes samples between different clusters mutually exclusive and samples among the same cluster attractive to each other. Meanwhile, the decoding networks are employed to reconstruct the feature representation, and further preserve the structural information among the original cross-modal data. To the end, we demonstrate the effectiveness of the proposed iCmSC via extensive experiments, which can justify that iCmSC achieves consistently large improvement compared with the state-of-the-arts.

For cross-modal subspace clustering, the key point is how to exploit the correlation information between cross-modal data. However, most hierarchical and structural correlation information among cross-modal data cannot be well exploited due to its high-dimensional non-linear property. To tackle this problem, in this paper, we propose an unsupervised framework named Cross-Modal Subspace Clustering via Deep Canonical Correlation Analysis (CMSC-DCCA), which incorporates the correlation constraint with a self-expressive layer to make full use of information among the inter-modal data and the intra-modal data. More specifically, the proposed model consists of three components: 1) deep canonical correlation analysis (Deep CCA) model; 2) self-expressive layer; 3) Deep CCA decoders. The Deep CCA model consists of convolutional encoders and correlation constraint. Convolutional encoders are used to obtain the latent representations of cross-modal data, while adding the correlation constraint for the latent representations can make full use of the information of the inter-modal data. Furthermore, self-expressive layer works on latent representations and constrain it perform self-expression properties, which makes the shared coefficient matrix could capture the hierarchical intra-modal correlations of each modality. Then Deep CCA decoders reconstruct data to ensure that the encoded features can preserve the structure of the original data. Experimental results on several real-world datasets demonstrate the proposed method outperforms the state-of-the-art methods.