With the deep integration of wearable devices and information technology, health data collection has become more efficient and accurate. This has brought about significant changes to health management and public health. However, due to the high sensitivity and privacy of health data, data sharing faces major challenges. Most existing studies focus on encryption algorithms and access control to ensure data security. They often ignore the credibility of data providers, which affects data quality and reduces user participation. To address these issues, this article proposes a blockchain-based and reputation-enhanced health data trading model. A fine-grained reputation value calculation method based on the Beta distribution is introduced to objectively evaluate the behavior of data providers. Based on this, a consensus mechanism linked to reputation value is designed to improve consensus efficiency and avoid centralization of node selection. Furthermore, this article uses evolutionary game theory to analyze the reward and punishment mechanism in the trading process. It explores the dynamic balance between platform cost and user willingness to share. Experimental results show that the model ensures secure health data sharing, while effectively improving data usability, system fairness, and user participation.

General image encryption schemes transform plaintext images into snowflake-like ciphertext patterns through efficient cryptographic permutation and confusion primitives. However, these encryption schemes cannot achieve the goal of privacy computing that data are available but not visible. Thumbnail-preserving encryption (TPE) not only ensures the privacy of ciphertext images, but also allows legitimate users of cloud servers to search through thumbnails in the ciphertext image space. But as far as we know, there has not been a detailed security analysis of TPE schemes up to now.In this paper, we propose a universal attack framework, called AttackTPE, based on pix2pix generative adversarial network under known-plaintext attack (KPA) model. The experimental results demonstrate that the average structural similarity (SSIM) between decrypted images and plaintext images of different block sizes ranges from 0.7024 to 0.8219, and the average peak signal-to-noise ratio (PSNR) between them ranges from 18.6825 to 24.2575. Additionally, the smaller the block size of ciphertext images, the higher the SSIM and PSNR.

The popularity of smart homes has been facilitated by the development of information and communication technology and wireless sensors. These technologies allow users to remotely control smart devices in their homes. However, smart home communication primarily relies on the Internet, which exposes smart home networks to multiple security risks, such as smart device capture, simulation attacks, and internal privilege attacks. These threats could result in illegal users stealing data transmitted by smart devices, thereby violating users’ privacy and security. To address these challenges, this article proposes a novel revocable biometric authentication scheme based on secret sharing technology for secure remote user authentication in smart home environments. This solution introduces secret sharing technology to protect users’ revocable biometric templates and enhances the security of user privacy. To verify the effectiveness of the proposed scheme, we conducted experimental verification, including measuring the equal error rate (EER), unlinkability, and revocability. The experimental results demonstrate that this solution can effectively protect the security of user biometric information while maintaining high authentication accuracy. Additionally, it has good revocability and unlinkability, which helps to improve the security and safety of smart homes without compromising user experience.

Wireless body area networks (WBANs) are widely used in the medical field, which makes it easier for doctors to gather patients’ physiological information to diagnose their physical status. Recently, a variety of lightweight authentication key agreement (AKA) protocols have been proposed for WBANs. However, the computational overhead of the authentication process for most of these protocols increases exponentially when the number of patients increases dramatically. Therefore, to improve the authentication efficiency, a many-to-one, lightweight batch AKA protocol based on the Chinese remainder theorem is proposed, which can realize that cloud server (CS) interacts with the gateway nodes once to obtain multiple session keys with different sensor nodes, so as to achieve the effect that multiple sensor nodes participate in the AKA at the same time. In addition, only hash function, XOR operation, and symmetric encryption are used in the proposed protocol. During the key agreement process, CS can compute the session key between it and the sensor node on demand. Sensor nodes can dynamically join and leave the WBAN, which increases the flexibility of the protocol. We verify the protocol’s security using formal security analysis tools, such as the real-or-random model, Burrows-Abadi–Needham logic, and the ProVerif tool. The experiment and comparison results indicate that the proposed protocol outperforms the other related protocols in terms of efficiency.

The fifth-generation (5G) networks can provide high data rates, ultralow latency and huge network capacity. In 5G networks environment, the popularity of the Internet of Things (IoT) has led to a rapid increase in the amount of data. Multiserver distributed cloud computing technology provides an excellent solution to alleviate network pressure caused by the rapid growth of data. However, this technology serves as a two-edged weapon, which not only makes various IoT applications possible but also brings growing concerns for user privacy and ever pressing security challenges. To ensure the high security of 5G network-based applications, we design a secure user anonymity-preserving biometrics and PUFs-based multiserver authentication scheme with key agreement. In our method, we make full use of the inherent security features of user fingerprint and smart device PUF to design a secure multiserver authentication scheme with key agreement in 5G Networks. The proposed scheme is able to resist recognized attacks and its robustness has been verified by security analysis.

The sharing of pathological data is of utmost importance in various applications such as remote diagnosis, graded diagnosis, illness treatment, and specialist system development. However, ensuring reliable, secure, privacy-preserving, and efficient sharing of pathological data pose significant challenges. This paper presents a novel solution that leverages blockchain technology to ensure reliability in pathological data sharing. Additionally, it employs conditional proxy re-encryption (C-PRE) and public key encryption with equality test technology to control the scope and preserve the privacy of shared data. To assess the practicality of our solution, we have implemented a prototype system using Hyperledger Fabric and conducted evaluations with various metrics. We also compared the solution with relevant schemes. The results demonstrate that the proposed solution effectively meets the requirements for pathological data sharing and is practical in production scenarios.

Blockchain is commonly employed in access control to provide safe medical data exchange because of the characteristics of decentralization, nontamperability, and traceability. Patients share personal health data by granting access rights to users or medical institutions. The major purpose of the existing access control techniques is to identify users who are permitted to access medical data. They hardly ever recognize internal assailants from legitimate entities. Medical data will involve multilayer access within the authorized organizations. Considering the cost of permissions management and the problem of insider malicious node attacks, users hope to implement authorization constraints within the authorized institutions. It can prevent their data from being maliciously disclosed by end‐users from different authorized healthcare domains. For the purpose to achieve the fine‐grained permissions propagation control of medical data in sharing institutions, a trust‐based authorization access control mechanism is suggested in this study. Trust thresholds are assigned to different privileges based on their sensitivity and used to generate zero‐knowledge proof to be broadcasted among blockchain nodes. This method evaluates the trust of each user through the dynamic trust calculation model. And meanwhile, smart contract is employed to verify whether the user’s trust can activate some permissions and ensure the privacy of the user’s trust in the process of authorization verification. In addition, the authorization transaction between users and institutions is recorded on the blockchain for patient traceability and accountability. The feasibility and effectiveness of the scheme are demonstrated through comprehensive comparisons and extensive experiments.

Thumbnail-preserving encryption (TPE) is one of the prominent cryptographic primitives to balance the usability and privacy for cloud images. Ideal TPE requires that the thumbnail of the plaintext image is exactly the same as that of the ciphertext. Although there are some methods to improve the efficiency of ideal TPE, the results are not satisfactory due to the existing methods being within the time-consuming rank-encipher framework. Combined with face detection algorithms, we introduce a new framework, called Detect-TPE, for constructing efficient ideal TPE schemes in this article. The framework uses several classic detection algorithms to find the region of privacy and then encrypts the detected regions by ideal TPE. We use many different face detection algorithms (e.g., BlazeFace and YOLOv5-Face), and found that the main factors affecting the execution time of Detect-TPE are the size of block, the accuracy, and the efficiency of the used face detection algorithm. The experimental results show that if we use deep neural network (DNN) for face detection and the size of block is 64, the encryption time is reduced by 55.84% and the space occupied by the ciphertext image is reduced by 35.27% compared with existing TPE schemes. Additionally, the proposed Detect-TPE framework can resist facial detection attacks if the size of the block is greater than 32, and the probability of success in resisting this attack exceeds 99.22%.

In a mobile edge computing environment, the computing tasks of resource-constrained IoT devices are often offloaded to mobile edge computing servers for processing. In order to ensure the security of the task offloading process, both parties need to perform mutual authentication and negotiate a session key first. The security defenses in the existing authentication schemes are often only aimed at external attackers, while ignoring the possible malicious behaviors of semi-trusted servers. Furthermore, they cannot effectively take into account the device-side lightweight and security, as well as the load problem of a single registry. In this paper, we propose a new anonymous authentication key agreement scheme that fully considers the resource constraints of terminal devices and the security risks of semi-trusted servers. In the scheme, we use the method of generating pairing information during registration to avoid the server-side directly contacting the user’s private information, and support trusted third parties not to participate in the authentication process. In addition, by setting up authentication servers to outsource computing tasks, the device-side can avoid blindly selecting a computing server for task offloading, achieve accurate task assignment and efficient execution of authentication. We use Real-Or-Random model and BAN logic to demonstrate the security of the proposed scheme, and use the ProVerif tool to verify its authenticated reachability and confidentiality. Compared with other schemes with the same structure, this scheme is superior to similar schemes, and has higher security on the basis of ensuring the least amount of computation on the device-side.

This article discusses the application of wireless sensor networks (WSNs) in industrial settings, particularly in the context of the Internet of Things. The advantages of WSNs, such as convenience, low energy consumption, and real-time monitoring, make them highly suitable for industrial applications. However, these networks face several challenges, including signal interference, security concerns, privacy issues, and battery life. This article explores these challenges and proposes potential solutions. Current industrial wireless sensor protocols often prioritize lightweight or real-time aspects, potentially neglecting security considerations. This article proposes a strategy for protecting user biometrics during authentication using cancelable biometrics. The approach utilizes sliding window hashing, fuzzy extractors, and physical unclonable functions to generate a cancelable biometric template. The proposed industrial wireless sensor protocols will be used in this strategy. This protocol aims to safeguard users' biometric information in the event of a data breach while detecting and preventing potential attacks on the sensors.

With the widespread popularity of smartphones, watches, and other devices, mobile crowd sensing has garnered significant public attention. Application service providers publish crowd sensing tasks, and users actively participate in collecting relevant sensing data, which are then submitted to servers. However, these data contain users’ personal privacy. Therefore, this article proposes a trajectory privacy protection method based on differential privacy(CTDP). First, the article conducts clustering based on the features of user trajectory data to extract feature regions of the user trajectory. Then, a personalized privacy budget allocation method is developed based on the number of trajectory points in the feature region and the user's privacy requirements for sensitive trajectory points. A set of confusion points is generated within the feature range and a score is calculated based on its similarity to the trajectory points. Subsequently, the sampling probability is calculated based on the score and privacy budget of each confusion point, and finally the confusion points are selected through random sampling. The internationally recognized real dataset Cabspotting data was used for experimental evaluation. The experimental results indicate that the method proposed in this article exhibits excellent performance in terms of data availability while providing sufficient privacy guarantees.

With the continuous innovative development and popularization of mobile smart devices , the application of Mobile Crowd Sensing (MCS) continues to be studied extensively. However, existing centralized MCS applications that use servers for task publishing and data collection exhibit common problems, such as single points of failure and security vulnerabilities . Accordingly, we proposed a hybrid blockchain-based identity authentication scheme for MCS called HBIA, which uses blockchain technology to resolve the single-point failure problem. HBIA builds a cluster structure based on factors such as geographical location and balance, and uses it to construct a hybrid blockchain , with the cluster head node and internal cluster node authenticating on the public and private chains, respectively. We also implemented zero-knowledge proof (ZKP) to ensure the privacy of participants’ identities, thus balancing the contradiction between blockchain transparency and security. In addition, HBIA uses the zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK) technology to enable off-chain computing and on-chain verification, further reducing the blockchain’s workload. Finally,​ HBIA was evaluated based on the pavement crack detection task and tested on the Ethereum public test network known as Ropsten. The test results indicate that the identity authentication scheme proposed in this paper is superior to existing schemes in terms of authentication time.

In mobile edge computing, the computing tasks of IoT terminal devices with limited computing power often need to be offloaded to servers for processing. However, there are malicious attacks by adversaries and malicious behaviors of servers in the network, coupled with the use of insecure network channels for data information transmission. These factors seriously threaten the privacy and data security of terminal devices and users. Therefore, it is urgent to use a safe and efficient anonymous authentication key agreement mechanism to verify the legitimacy of the identities of computing participants and ensure the safe transmission of task data. Recently Jia et al. proposed an identity-based authentication scheme, which combines many advantages of previous work and is resistant to various attacks. However, we found that their scheme has security problems, such as offline key guessing attack, internal attack, and user anonymity problems. We classify them as semi-trusted server attacks. In order to solve these security problems, we propose an improved scheme to better realize the authentication function by using flexible and security-enhanced keys for terminal equipment (TE), while ensuring the anonymity of the TE through implicit ID. Furthermore, we provide formal security proof, formal security verification, and security analysis for the improved protocol. Compared with the previous scheme, the scheme has certain improvements in security and performance.

A medical cyber–physical system (MCPS) is a unique cyber–physical system (CPS), which combines embedded software control devices, networking capabilities, and complex physiological dynamics of patients in the modern medical field. In the process of communication, device, and information system interaction of MCPS, medical cyber–physical data are generated digitally, stored electronically, and accessed remotely by medical staff or patients. With the advent of the era of medical big data, a large amount of medical cyber–physical data is collected, and its sharing provides great value for diagnosis, pathological analysis, epidemic tracking, pharmaceutical, insurance, and so on. This overview will present MCPS’s architectures and frameworks from different perspectives, modeling and verification methods, identification and sign sensing technologies, key communications’ technologies, data storage and analysis technologies, monitoring systems, data security and privacy protection technologies, and key research perspectives and directions. We can have a comprehensive understanding of the important characteristics and technical route of MCPS, and grasp its research status and progress.

As the background of application in the field of smart health care, the flexible interaction between patients and medical system is provided by medical cyber-physical systems (MCPSs) to realize all-round three-dimensional medical service. According to the controllable and credible requirements of MCPS, it needs a secure and reliable device identity authentication mechanism to build the security barrier. Based on the blockchain technology, a lightweight authentication scheme is designed for sensor/execution devices, users, and gateway nodes in MCPS. The security analysis and experimental results show that the scheme can resist the existing attacks with better efficiency; thus, our proposed scheme can be efficiently applied to the medical field.

Abstract A digital signature scheme allows a signer to sign electronic messages using his or her secret key, and any verifier can validate the correctness according to a given verification procedure. Although a variety of lattice‐based signature schemes have been proposed in the past few years, there does not exist a scheme that has short signatures and constant‐size public keys simultaneously. In this paper, we propose a new method for constructing short lattice signatures with constant‐size public keys in the standard model. In our scheme, each signature contains a low‐dimensional lattice vector and the public key only contains three matrices plus a vector. Compared with previous constructions, our scheme is very simple and does not require any complex homomorphic computation. In order for security proof to work, we introduce a new hard lattice problem, called variant small integer solution (Variant‐SIS), and give the security reduction from small integer solution to Variant‐SIS. Then we define a family of hash functions based on the hardness of Variant‐SIS and prove its security properties, including one‐wayness and collision resistance. As a matter of independent interest, the proposed hard problem may be useful in many other lattice‐based cryptographic constructions. Last, the comparison with similar works demonstrates the superiority of our scheme. Copyright © 2016 John Wiley & Sons, Ltd.