Steganography is a popular research direction in the field of information security. Due to the widespread use of video media, video steganography has received much attention from the research community. Among video steganography, motion vector (MV)-based video steganography has become one of the critical concerns of researchers due to its large embedding capacity and high visual quality. In this article, we focus on the research of MV-based video steganography. Firstly, the basic principles and evaluation criteria for MV-based steganography are discussed. Secondly, according to the different technical characteristics, the MV-based steganography is divided into three categories: the traditional MV domain steganography, the code-based MV domain steganography, and the adaptive MV domain steganography based on the framework of minimizing embedding distortion. The advantages and possible improvement directions of the above representative methods are illustrated. And then, the MV-based video steganalysis is outlined according to different perspectives of feature extraction, which is conducive to the design of better steganography algorithms. Finally, five future research directions are presented, such as designing distortion functions based on multiple factors, embedding methods based on new video coding standards, deep learning-based MV steganography, multidomain embedding strategies, and moving the MV-based steganography from the laboratory into the real world.

With the increasing emphasis on security and privacy, video in the cloud sometimes needs to be stored and processed in an encrypted format. To facilitate the indexing and tampering detection of encrypted videos, data hiding is performed in encrypted videos. This paper proposes a novel separable scheme for encryption and reversible data hiding. In terms of encryption method, intra-prediction mode and motion vector difference are encrypted by XOR encryption, and quantized discrete cosine transform block is permutated based on logistic chaotic mapping. In terms of the reversible data hiding algorithm, difference expansion is applied in encrypted video for the first time in this paper. The encryption method and the data hiding algorithm are separable, and the embedded information can be accurately extracted in both encrypted video bitstream and decrypted video bitstream. The experimental results show that the proposed encryption method can resist sketch attack and has higher security than other schemes, keeping the bit rate unchanged. The embedding algorithm used in the proposed scheme can provide higher capacity in the video with lower quantization parameter and good visual quality of the labeled decrypted video, maintaining low bit rate variation. The video encryption and the reversible data hiding are separable and the scheme can be applied in more scenarios.

With the rapid development of cloud storage and cloud computing technology, users tend to store data in the cloud for more convenient services. In order to ensure the integrity of cloud data, scholars have proposed cloud data integrity verification schemes to protect users’ data security. The storage environment of the Internet of Things, in terms of big data and medical big data, demonstrates a stronger demand for data integrity verification schemes, but at the same time, the comprehensive function of data integrity verification schemes is required to be higher. Existing data integrity verification schemes are mostly applied in the cloud storage environment but cannot successfully be applied to the environment of the Internet of Things in the context of big data storage and medical big data storage. To solve this problem when combined with the characteristics and requirements of Internet of Things data storage and medical data storage, we designed an SM2-based offline/online efficient data integrity verification scheme. The resulting scheme uses the SM4 block cryptography algorithm to protect the privacy of the data content and uses a dynamic hash table to realize the dynamic updating of data. Based on the SM2 signature algorithm, the scheme can also realize offline tag generation and batch audits, reducing the computational burden of users. In security proof and efficiency analysis, the scheme has proven to be safe and efficient and can be used in a variety of application scenarios.

The decline in genome sequencing costs has widened the population that can afford its cost and has also raised concerns about genetic privacy. Kim et al. present a practical solution to the scenario of secure searching of gene data on a semitrusted business cloud. However, there are three errors in their scheme. We have made three improvements to solve these three errors. (1) They truncate the variation encodings of gene to 21 bits, which causes LPCE error and more than 5% of the entries in the database cannot be queried integrally. We decompose these large encodings by 44 bits and deal with the components, respectively, to avoid LPCE error. (2) We abandon the hash function used in Kim’s scheme, which may cause HCE error with a probability of 2-22 and decompose the position encoding of gene into three parts with the basis 211 to avoid HCE error. (3) We analyze the relationship between the parameters and the CCE error and specify the condition that parameters need to satisfy to avoid the CCE error. Experiments show that our scheme can search all entries, and the probability of searching error is reduced to less than 2-37.4.

Side-channel attacks on cryptographic chips in embedded systems have been attracting considerable interest from the field of information security in recent years. Many research studies have contributed to improve the side-channel attack efficiency, in which most of the works assume the noise of the encryption signal has a linear stable Gaussian distribution. However, their performances of noise reduction were moderate. Thus, in this paper, we describe a highly effective data-preprocessing technique for noise reduction based on empirical mode decomposition (EMD) and demonstrate its application for a side-channel attack. EMD is a time-frequency analysis method for nonlinear unstable signal processing, which requires no prior knowledge about the cryptographic chip. During the procedure of data preprocessing, the collected traces will be self-adaptably decomposed into sum of several intrinsic mode functions (IMF) based on their own characteristics. And then, meaningful IMF will be reorganized to reduce its noise and increase the efficiency of key recovering through correlation power analysis attack. This technique decreases the total number of traces for key recovering by 17.7%, compared to traditional attack methods, which is verified by attack efficiency analysis of the SM4 block cipher algorithm on the FPGA power consumption analysis platform.

Nowadays, the location privacy problem has become an important problem for the users who enjoy the location-based services (LBSs). Researchers have focused on the problem of how to protect the location privacy of user efficiently for a long time. On one hand, many achievements adopt the centralized structure in which there is an additional center server. Additionally, some other researchers adopt the distributed structure to overcome the disadvantages brought by the center server in the centralized anonymous system structure. On the other hand, the existing methods of solving the problem are always to protect the individual user’s location privacy in LBSs, without considering the user group’s location privacy. This kind of methods is not very applicable to the status of a number of users who formed a group to complete a LBS task together by collaborative computing. In order to solve the problem of location privacy protection for a user group in the untrusted mobile social networks, a location privacy protection method based on the distributed structure is discussed in this paper. In the scheme, the special homomorphic features of BGN cryptosystem are cleverly used so that it can solve the group’s three classical location service applications simultaneously, namely, group nearest neighbor query, optimal group collection point determination, and group friend’s distance query, by only one security policy. If there are k users who formed the group, it could achieve k-anonymity without exposing the coordinate of each individual user or using any anonymous areas. Furthermore, theoretical and experimental analysis proves that the proposal can efficiently protect each user’s location privacy in the group through taking full advantage of the collaborative computing and communication capabilities of the mobile terminals. It can resist the existing distance interaction attack and collusion attack and can realize the secure and efficient fine-grained controllable location privacy protection for the user group.