In current healthcare systems, electronic medical records (EMRs) are always located in different hospitals and controlled by a centralized cloud provider. However, it leads to single point of failure as patients being the real owner lose track of their private and sensitive EMRs. Hence, this paper aims to build an access control framework based on smart contract, which is built on the top of distributed ledger (blockchain), to secure the sharing of EMRs among different entities involved in the smart healthcare system. For this, we propose four forms of smart contracts for user verification, access authorization, misbehavior detection, and access revocation respectively. In this framework, considering the block size of ledger and huge amount of patient data, the EMRs are stored in cloud after being encrypted through the cryptographic functions of Elliptic Curve Cryptography (ECC) and Edwards-Curve Digital Signature Algorithm (EdDSA), while their corresponding hashes are packed into blockchain. The performance evaluation based on a private Ethereum system is used to verify the efficiency of proposed access control framework in the real-time smart healthcare system.

In the research of multi-image encryption (MIE), the image type and size are important factors that limit the algorithm design. For this reason, the multi-image (MI) hybrid encryption algorithm that can flexibly encrypt color images and grayscale images of various sizes is proposed. Based on this, combining the back propagation (BP) neural network compression technology and the MI hybrid encryption algorithm, an MI hybrid compression–encryption (MIHCE) scheme can be obtained to reduce the pressure of simultaneous transmission and storage of multiple cipher images. Besides, two chaotic maps are used in the scheme design process. By plotting the phase diagrams under different parameter conditions, the rich variation of the behavior of the chaotic maps in the phase space is exhibited. The MIHCE scheme based on the chaotic maps consists of three parts: 1) compressing the MI cube by using the BP neural network; 2) scrambling the compressed MI cube based on the knight tour problem and chaotic sequences; and 3) diffusing the scrambled MI cube. After the MIHCE is completed, the obtained cipher images are stored and transmitted. Subsequently, the security analysis and compression performance analysis prove the feasibility and safety of the designed compression–encryption scheme.

At present, discrete memristor is a focus of chaotic mapping. However, the study of discrete memcapacitor and discrete meminductor has not begun. In order to study the dynamics of discrete meminductor and the dynamics of chaotic map containing discrete meminductor, in this article, a 5-D Hyperchaotic mapping is constructed by combining discrete memristor and discrete meminductor. In addition, the dynamical behavior of chaotic mapping is analyzed by means of the phase diagram of a chaotic attractor, lyapunov exponents (LEs), bifurcation diagram, and spectral entropy complexity. Theoretical analysis and numerical simulation show that the 5-D chaotic map not only has hyperchaotic phenomenon, but also can generate different types of chaotic attractors with the change of parameters. Especially, the special phenomenon of coexistence of attractors is discovered. This makes this 5-D hyperchaotic map more suitable for many chaotic-based engineering applications. So, an application of discrete chaotic system in chaotic image encryption is proposed. The security and reliability of the encryption scheme are analyzed by national institute of standards and technology (NIST) test, entropy, correlation, and histogram. Finally, the results of the numerical simulation are verified by a digital circuit. Due to its complex dynamical characteristics, the 5-D hyperchaotic map has great value in engineering applications.

With the popularity of cloud storage services, people are increasingly accustomed to storing images in the cloud. However, cloud storage services raise privacy concerns, e.g., leakage of images to unauthorized third parties and service providers may exploit image detection technologies to portrait users without permission. Although privacy concerns can be solved by encrypting images before they are uploaded to the cloud, traditional encryption methods significantly affect the usability and user experience, for example, users cannot preview images in the cloud. Recently, Marohn et al. proposed two approximate thumbnail-preserving encryption schemes, called DRPE and TPE-LSB, to balance the privacy and usability of images in the cloud. However, both schemes have defects that either the decryption may fail or the ciphertext images have poor performance in perceived quality and too many noise points after decryption. To this end, we pertinently propose a high-fidelity thumbnail-preserving encryption scheme (HF-TPE). Compared with the previous works, on the one hand, the HF-TPE scheme not only ensures the correct decryption of ciphertext images, but also makes the ciphertext thumbnails more close to the plaintext images perceptually. On the other hand, the decrypted thumbnails have lower noise intensity and upper limit of the number of noises. In addition, simulation experiments further show that the HF-TPE scheme can guarantee users’ usability.

The advent of artificial intelligence-generated content (AIGC) represents a pivotal moment in the evolution of information technology. With AIGC, it can be effortless to generate high-quality data that is challenging for the public to distinguish. Nevertheless, the proliferation of generative data across cyberspace brings security and privacy issues, including privacy leakages of individuals and media forgery for fraudulent purposes. Consequently, both academia and industry begin to emphasize the trustworthiness of generative data, successively providing a series of countermeasures for security and privacy. In this survey, we systematically review the security and privacy on generative data in AIGC, particularly for the first time analyzing them from the perspective of information security properties. Specifically, we reveal the successful experiences of state-of-the-art countermeasures in terms of the foundational properties of privacy, controllability, authenticity, and compliance, respectively. Finally, we show some representative benchmarks, present a statistical analysis, and summarize the potential exploration directions from each of these properties.