A Secure Implementation of a Symmetric Encryption Algorithm in White-Box Attack Contexts

In a white-box context, an adversary has total visibility of the implementation of the cryptosystem and full control over its execution platform. As a countermeasure against the threat of key compromise in this context, a new secure implementation of the symmetric encryption algorithm SHARK is proposed. The general approach is to merge several steps of the round function of SHARK into table lookups, blended by randomly generated mixing bijections. We prove the soundness of the implementation of the algorithm and analyze its security and efficiency. The implementation can be used in web hosts, digital right management devices, and mobile devices such as tablets and smart phones. We explain how the design approach can be adapted to other symmetric encryption algorithms with a slight modification

Smart Swap for more efficient clustering

Abstract-Local search algorithms, such as randomized and deterministic swap-based clustering, are often used for solving clustering problem. In this paper, we propose a new swap-based local search algorithm, Smart Swap, which preserves the stability of the previous solutions but is more efficient. It performs the swap by finding the nearest pair among the centroids and sorting the clusters by their distortion values. Then it swaps one of the nearest pair centroids to any position in that cluster. K-means iteration is employed to repartition the dataset and to fine-tune the swapped solution. The algorithm is easy to implement and iterates less than the previous swap based local search algorithms. Experiments show that the proposed algorithm keeps at least 97 % stability for the synthetic datasets and 0.577 of standard deviation for the real data. It is also much faster than the other swap-based algorithms. I

Secure Obfuscation for Encrypted Group Signatures.

In recent years, group signature techniques are widely used in constructing privacy-preserving security schemes for various information systems. However, conventional techniques keep the schemes secure only in normal black-box attack contexts. In other words, these schemes suppose that (the implementation of) the group signature generation algorithm is running in a platform that is perfectly protected from various intrusions and attacks. As a complementary to existing studies, how to generate group signatures securely in a more austere security context, such as a white-box attack context, is studied in this paper. We use obfuscation as an approach to acquire a higher level of security. Concretely, we introduce a special group signature functionality-an encrypted group signature, and then provide an obfuscator for the proposed functionality. A series of new security notions for both the functionality and its obfuscator has been introduced. The most important one is the average-case secure virtual black-box property w.r.t. dependent oracles and restricted dependent oracles which captures the requirement of protecting the output of the proposed obfuscator against collision attacks from group members. The security notions fit for many other specialized obfuscators, such as obfuscators for identity-based signatures, threshold signatures and key-insulated signatures. Finally, the correctness and security of the proposed obfuscator have been proven. Thereby, the obfuscated encrypted group signature functionality can be applied to variants of privacy-preserving security schemes and enhance the security level of these schemes

P.: Keyword clustering for automatic categorization

Abstract Processing short texts is becoming a trend in infor

A Dual-Stage Attention Model for Tool Wear Prediction in Dry Milling Operation

The intelligent monitoring of tool wear status and wear prediction are important factors affecting the intelligent development of the modern machinery industry. Many scholars have used deep learning methods to achieve certain results in tool wear prediction. However, due to the instability and variability of the signal data, some neural network models may have gradient decay between layers. Most methods mainly focus on feature selection of the input data but ignore the influence degree of different features to tool wear. In order to solve these problems, this paper proposes a dual-stage attention model for tool wear prediction. A CNN-BiGRU-attention network model is designed, which introduces the self-attention to extract deep features and embody more important features. The IndyLSTM is used to construct a stable network to solve the gradient decay problem between layers. Moreover, the attention mechanism is added to the network to obtain the important information of output sequence, which can improve the accuracy of the prediction. Experimental study is carried out for tool wear prediction in a dry milling operation to demonstrate the viability of this method. Through the experimental comparison and analysis with regression prediction evaluation indexes, it proves the proposed method can effectively characterize the degree of tool wear, reduce the prediction errors, and achieve good prediction results

Inter- and Intra-Modal Contrastive Hybrid Learning Framework for Multimodal Abstractive Summarization

Internet users are benefiting from technologies of abstractive summarization enabling them to view articles on the internet by reading article summaries only instead of an entire article. However, there are disadvantages to technologies for analyzing articles with texts and images due to the semantic gap between vision and language. These technologies focus more on aggregating features and neglect the heterogeneity of each modality. At the same time, the lack of consideration of intrinsic data properties within each modality and semantic information from cross-modal correlations result in the poor quality of learned representations. Therefore, we propose a novel Inter- and Intra-modal Contrastive Hybrid learning framework which learns to automatically align the multimodal information and maintains the semantic consistency of input/output flows. Moreover, ITCH can be taken as a component to make the model suitable for both supervised and unsupervised learning approaches. Experiments on two public datasets, MMS and MSMO, show that the ITCH performances are better than the current baselines

An application in a privacy-preserving emergency call system based on mobile social network.

<p>An application in a privacy-preserving emergency call system based on mobile social network.</p

Security notions of group signature schemes.

<p>Security notions of group signature schemes.</p

The activity diagram.

<p>The activity diagram.</p