A Multi-Receiver ID-Based Generalized Signcryption Scheme

Generalized signcryption(GSC) can adaptively work as an encryption scheme, a signature scheme or a signcryption scheme with only one algorithm. In this paper, the formal definition and security notions of multi-receiver identity-based generalized signcryption (MID-GSC) are defined. A concrete scheme is also proposed and proved to be confidential under the Bilinear Diffie-Hellman (BDH) assumption and existential unforgeable under the Computational Diffie-Hellman(CDH) assumption in the random oracle model, which only needs one pairing computation to generalized signcrypt a single message for n receivers using the randomness re-use technique. Compared with other multi-receiver ID-based signcryption schemes, the new scheme is also of high efficiency

An Efficient Feature Extraction Scheme for Mobile Anti-Shake in Augmented Reality

In recent years, augmented reality on mobile devices has become popular. Mobile shakes are the most typical type of interference in mobile augmented reality. To negate such interference, anti-shake is an urgent requirement. To enhance anti-shake efficiency, we propose an efficient feature extraction scheme for mobile anti-shake in augmented reality. The scheme directly detects corners to avoid the non-extreme constraint such that the efficiency of feature extraction is improved. Meanwhile, the scheme only updates the added corners during mobile shakes, which improves the accuracy of feature extraction. In the experiments, the memory consumption of existing methods is almost double compared to that in our scheme. Further, the runtime of our scheme is only half of the runtime of the existing methods. The experimental results demonstrate that our scheme performs better than the existing classic methods on mobile anti-shake in terms of memory consumption, efficiency, and accuracy

Reversible Watermarking Using Prediction-Error Expansion and Extreme Learning Machine

Currently, the research for reversible watermarking focuses on the decreasing of image distortion. Aiming at this issue, this paper presents an improvement method to lower the embedding distortion based on the prediction-error expansion (PE) technique. Firstly, the extreme learning machine (ELM) with good generalization ability is utilized to enhance the prediction accuracy for image pixel value during the watermarking embedding, and the lower prediction error results in the reduction of image distortion. Moreover, an optimization operation for strengthening the performance of ELM is taken to further lessen the embedding distortion. With two popular predictors, that is, median edge detector (MED) predictor and gradient-adjusted predictor (GAP), the experimental results for the classical images and Kodak image set indicate that the proposed scheme achieves improvement for the lowering of image distortion compared with the classical PE scheme proposed by Thodi et al. and outperforms the improvement method presented by Coltuc and other existing approaches

An Efficient Top-k Query Scheme Based on Multilayer Grouping

The top-k query is to find the k data that has the highest scores from a candidate dataset. Sorting is a common method to find out top-k results. However, most of existing methods are not efficient enough. To remove this issue, we propose an efficient top-k query scheme based on multilayer grouping. First, we find the reference item by computing the average score of the candidate dataset. Second, we group the candidate dataset into three datasets: winner set, middle set and loser set based on the reference item. Third, we further group the winner set to the second-layer three datasets according to k value. And so on, until the data number of winner set is close to k value. Meanwhile, if k value is larger than the data number of winner set, we directly return the winner set to the user as a part of top-k results almost without sorting. In this case, we also return the top results with the highest scores from the middle set almost without sorting. Based on above innovations, we almost minimize the sorting. Experimental results show that our scheme significantly outperforms the current classical method on the performance of memory consumption and top-k query

Cryptanalysis and Improvement of a Multi-Receiver Generalized Signcryption Scheme

Abstract. Generalized signcryption (GSC) scheme can adaptively work as an encryption scheme, a signature scheme or a signcryption scheme with only one algorithm. It is very suitable for storage-constrained environments. In this paper, we analyze a multi-receiver GSC scheme, and show that it cannot achieve indistinguishability-adaptive chosen ciphertext attack (IND-CCA2) secure in the pure encryption mode and hybrid encryption mode. We further propose a revised version of the scheme, which resolves the security issues of the original scheme without sacrificing its high efficiency and simple design. Our improved scheme can be proved to be IND-CCA2 secure and existentially unforgeable-adaptive chosen message attack (EUF-CMA) under computational Diffie-Hellman (CDH) assumption

Certificateless Key-Insulated Generalized Signcryption Scheme without Bilinear Pairings

Generalized signcryption (GSC) can be applied as an encryption scheme, a signature scheme, or a signcryption scheme with only one algorithm and one key pair. A key-insulated mechanism can resolve the private key exposure problem. To ensure the security of cloud storage, we introduce the key-insulated mechanism into GSC and propose a concrete scheme without bilinear pairings in the certificateless cryptosystem setting. We provide a formal definition and a security model of certificateless key-insulated GSC. Then, we prove that our scheme is confidential under the computational Diffie-Hellman (CDH) assumption and unforgeable under the elliptic curve discrete logarithm (EC-DL) assumption. Our scheme also supports both random-access key update and secure key update. Finally, we evaluate the efficiency of our scheme and demonstrate that it is highly efficient. Thus, our scheme is more suitable for users who communicate with the cloud using mobile devices