Cloud storage is an ideal platform to accommodate massive data. However, with the increasing number of various devices and improved processing power, the amount of generated data is becoming gigantic. Therefore, this calls for a cost‐effective way to outsource massively generated data to a remote server. Cloud service providers utilise deduplication technique which deduplicates redundant data by aborting identical uploading requests and deleting redundant files. However, current deduplication mechanisms mainly focus on the storage saving of the server, and ignore the sustainable and long‐term financial interests of servers and users. This is not helpful to expand outsourcing and deduplication services. Blockchain is an ideal solution to achieve an economical and incentive‐driven deduplication system. Though some current research studiess have integrated deduplication with blockchain, they did not utilise blockchain as a financial tool. Meanwhile, it lacks an arbitration mechanism to settle disputes between the server and the user, especially in a Bitcoin payment where the payment is not confirmed immediately and a dispute may occur. This creates a burden to achieve fair and transparent incentive‐based deduplication service. In this work, we construct a deduplication system with financial incentives for the server and the user based on Bitcoin. The data owner will pay money via Bitcoin to the server for outsourcing the file, but this fee can be compensated by charging deduplication users with some fees to acquire the deduplication service. The server and the user can receive revenues using deduplication service. Disputes on the fair distribution of incentives can be settled by our arbitration protocol with chameleon hashes as arbitration tags. We give concrete construction and security requirements for our proposed . The security analysis shows that our is theoretically secure. The performance evaluation shows that our proposed is acceptably efficient for the deduplication. Meanwhile, we evaluate and conclude that 1% of outsourcing fee (or less) is a reasonable and preferable price for each deduplication user to pay as compensation for data owner.

This paper proposes a novel image compression‐encryption scheme based on compressive sensing and bit‐level XOR. In the proposed scheme, the encrypted sequences are generated from a 4‐D hyper‐chaotic Lorenz system and a Logistic system. First, an original image is sampled by a secure block parallel compressive sensing (PCS) scheme. In the PCS phase, a key‐controlled discrete cosine transform sparse basis matrix and a key‐controlled complex Hadamard measurement matrix are employed. Next, the real part and imaginary part of the resulting complex‐valued data are quantized and transformed into bit streams, respectively. After that, the two generated bit streams are combined into one bit stream. Then, the resulting bit stream is further encrypted by a bit‐level XOR operation, where the encrypted key is generated from a chaotic system. The experiment results show the effectiveness and reliability of the proposed joint compression‐encryption scheme. The proposed scheme can not only enhance the security of the compressed image but also improve the reconstructed quality of the compressed image.

Secret image sharing (SIS) is an important method to protect the security of secret images. When part of the shadows generated by the secret sharing is leaked or when the secret image is restored, the shadows and the secret need to be renewed. Aiming at secret image and shadow image renewal, a scheme for renewing secret image and shadow images is proposed. The scheme is to multiply the original and renewed shadow of the corresponding serial number to get a new shadow over finite fields. Our scheme can realise the renewal of the secret image and shadows and has characteristics of the variable threshold, secure public transmission, variable size, and lossless recovery. This paper theoretically analyses and proves the security of the proposed scheme and verifies the effectiveness through a large number of experiments.

The rapidly growing encrypted traffic hides a large number of malicious behaviours. The difficulty of collecting and labelling encrypted traffic makes the class distribution of dataset seriously imbalanced, which leads to the poor generalisation ability of the classification model. To solve this problem, a new representation learning method in encrypted traffic and its diversity enhancement model are proposed, which uses the diversity of images to represent the diversity of traffic samples. First, the encrypted traffic is transformed into Markov images. Then, a diversity maximisation Markov‐GAN based on the Simpson index is designed to generate new Markov images. Finally, the balanced Markov image set is sent to the CNN for classification. Experimental results show that the proposed method can predict the whole dataset space with only a few original samples. And the classification accuracies under different imbalance degrees are significantly improved, all of which are over 90%. The enhanced Markov image set can effectively alleviate performance generalisation deviation caused by different network depths. Even an ordinary CNN has almost the same classification effect as VGG13 and VGG16. Compared with other data enhancement methods, the Markov‐GAN only needs to balance the transform domain dataset, which is lightweight, easy to train and has stronger amplification ability.

This paper considers a problem of identifying matching attacks against Romulus‐M, one of the 10 finalists of National Institute of Standards and Technology Lightweight Cryptography standardisation project. Romulus‐M is provably secure, that is, there is a theorem statement showing the upper bound on the success probability of attacking the scheme as a function of adversaries' resources. If there exists an attack that matches the provable security bound, then this implies that the attack is optimal and that the bound is tight in the sense that it cannot be improved. It is shown that the security bounds of Romulus‐M are tight for a large class of parameters by presenting concrete matching attacks.