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.

With the exponential growth of mobile devices, Mobile Crowdsensing (MCS) has emerged as a new paradigm for various types of tasks. However, most existing studies focus primarily on the utility of either buyers or sellers, with limited exploration of the task types for buyers and the user types for sellers. Therefore, this paper investigates the incentive mechanisms for different types of sellers under varying task types. In this study, we classify sellers into two groups: teams and individuals, and classify buyers' tasks into two categories: simple tasks and complex tasks. Considering the characteristics of different task types and seller groups, we design two auction schemes: the Two-Stage Auction Scheme for Simple Tasks (TDAS-S) and the Two-Stage Auction Scheme for Complex Tasks (TDAS-C). In the first stage, we design a seller auction model based on the Stackelberg game to ensure the maximization of the seller's utility. In the second stage, we design an auction model for buyers, utilizing a variant of the Vickrey Auction and marginal contribution theory to pay the selected sellers in both TDAS-S and TDAS-C, ensuring the maximization of the buyer's utility. We further prove that the proposed scheme satisfies properties such as individual rationality, truthfulness, and budget balance. Finally, through extensive experiments on real-world datasets, we demonstrate that our scheme effectively balances the utility conflicts between buyers and sellers, allowing each party to maximize their respective utilities.

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 Internet of Medical Things (IoMT) is a key IoT application in e-health, where efficiency and user privacy are critical for cloud-based medical data storage and retrieval. Existing solutions often suffer from low retrieval efficiency and heightened privacy risks with large datasets. To improve search accuracy, support complex queries, and enhance data utility, we propose a novel medical data search algorithm using a Bloom filter for efficient multi-keyword search. The scheme employs homomorphic symmetric encryption to embed multiple keywords in ciphertexts, enabling multi-keyword comparison over encrypted data. Additionally, a balanced binary tree structure in non-leaf node indexing reduces unnecessary search overhead. Comprehensive analysis shows the scheme supports precise multikeyword queries and significantly outperforms prior approaches in computational and communication efficiency.

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 temporal decay of trust data in dynamic edge computing environments leads to inaccurate evaluation, and the recommended trust values from heterogeneous nodes are affected by subjective bias and are vulnerable to malicious attacks. To address these issues, this paper proposes a novel lightweight dynamic trust evaluation model. First, a time decay function is derived based on Newton’s law of cooling to effectively reflect the impact of trust timeliness on trust evaluation. On this basis, the real value is iteratively calculated and used as the recommended trust value through the truth discovery algorithm, enhancing the accuracy of trust evaluation. Then, the weight of the recommended trust value in the comprehensive trust value is determined based on the standard deviation of the weight of perceived data from each recommending node, balancing the influence of subjective and objective factors on trust evaluation results. Lastly, the comprehensive trust value is dynamically weighted, and an incentive mechanism is employed to update trust data based on feedback, reflecting the dynamic nature of trust. Theoretical analysis demonstrates that the model presented in this paper exhibits low time and space complexities, meeting the lightweight requirements of the edge computing environment. Experimental results indicate high recommendation trust accuracy and interaction success rates, as well as effective resistance against malicious attacks.

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.

With the proliferation of mobile smart devices, such as smartphones, mobile crowdsensing (MCS) has gained significant attention and widespread application. However, the increasing risk of personal privacy breaches has become a significant concern in MCS. Typically, workers are required to disclose their location information to participate in task assignments, making the protection of sensitive data, like location, a crucial factor influencing worker engagement. To address the issue of location privacy leakage in the task allocation process, this article proposes a location privacy protection method (VGDP) based on local differential privacy. In VGDP, the server utilizes a clustering algorithm to construct a task map based on the Voronoi diagram using task locations. Each task location is then mapped to its corresponding task area to ensure the privacy of the location information. Encoding technology is employed to encode the relative locations of all workers within the area, while a double random response mechanism is utilized to obfuscate the relative location codes, thereby safeguarding their location privacy. Furthermore, a personalized privacy budget allocation mechanism is employed to enhance the effectiveness of privacy protection. Once workers upload their perturbed location information to the server, the server selects winners based on the perturbed locations to facilitate task allocation. Additionally, this article proposes a high-reward payment method to augment workers’ enthusiasm for participation. Experimental results demonstrate that the proposed method exhibits promising performance in terms of data availability and location privacy.

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%.

Classroom learning is one of the main ways for students to acquire knowledge because the class usually has a large number of students and it is difficult for teachers to pay attention to the learning status of each student at the same time. Therefore, mastering the learning status of each student and dealing with it is the key factor to determine the quality of course teaching, and classroom actions can accurately reflect the learning state of students. Based on this, in this article, a passive radio frequency identification (RFID)-based classroom action recognition system LD-recognition is proposed. The system pastes the label on the right side of the desk, and the learning state of the students was judged by recognizing the four movements of raising the left hand, raising the right hand, nodding off, and holding the book. The system uses a multichannel attentional graph convolutional neural network (ATGCN) to deeply learn the phase and signal strength of actions and conduct action recognition. LD-recognition verifies the accuracy of actions from different distances, different experimenters, and different network models. The experimental results show that the recognition accuracy of LD-recognition system is high, reaching 96.9% on average.

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.

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.

Traditionally, RFID is frequently used in identification and localization. In this paper, an extension application of RFID is designed to recognize gestures. Currently, gesture recognition is mainly used for feature extraction through wearable sensors and video cameras, which have shortcomings such as inconvenience to carry and interference with obstacles. This paper proposes a gesture recognition system based on radio frequency identification (RFID), where users do not need to wear devices. In the proposed model, the interference information generated by the gesture action on the tag signal is used as the fingerprint feature of the action. To obtain satisfactory recognition, the signal diversity is first increased through the tag array. Then, the RSSI and phase signal are normalized to eliminate offset and noise before training. Furthermore, a residual neural network (ResNet) is carefully built as a gesture classification model. The experimental results show that the recognition system achieves more recognition accuracy than existing methods, and the average gesture recognition accuracy reaches 95.5%.

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.

Functional connectivity (FC) networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used in automated identification of brain disorders, such as Alzheimer's disease (AD) and attention deficit hyperactivity disorder (ADHD). To generate compact representations of FC networks, various thresholding methods have been designed for FC network analysis. However, these studies usually use a pre-defined threshold or connection percentage to threshold whole FC networks, thus ignoring the diversity of temporal correlation (e.g., strong associations) between brain regions in subject groups. In this work, we propose a distribution-guided network thresholding learning (DNTL) method for FC network analysis in brain disorder identification with rs-fMRI. Specifically, for each connection of a pair of brain regions, we propose to determine its specific threshold based on the distribution of connection strength (i.e., temporal correlation) between subject groups (e.g., patients and normal controls). The proposed DNTL can adaptively yield an FC-specific threshold for each connection in an FC network, thus preserving diversity of temporal correlation among different brain regions. Experiment results on 365 subjects from two datasets (i.e., ADNI and ADHD-200) suggest that the DNT method outperforms state-of-the-art methods in brain disorder identification with rs-fMRI data.

In the crowd sensing application, a slice-based trajectory privacy protection method (STPP) is proposed for the privacy of mobile object trajectory data with rich spatiotemporal information. The trajectory data is first sliced with the position (GPS) as the smallest tuple so that the trajectory is converted into a corresponding trajectory tuple set, and each trajectory tuple is attached with a unique identifier. The user generates three random numbers (α, λ, β) for each trajectory tuple, and sends the trajectory tuple together with (α, λ, β) to the next user who then sends the trajectory tuple and the changed three random numbers to another randomly selected user. In this way, when the threshold of the set random number is reached, the user uploads the trajectory tuple to the server. Thus, the attacker cannot distinguish the location of the user. Due to the loss of the clustering relationship, the uploaded trajectory tuple becomes a set of anonymous and noncontiguous data sets, so that the server can not recognize the individual motion trajectory at all. Finally, the user can reconstruct the trajectory on the client based on the unique identifier. The experimental results and theoretical analysis show that STPP protects the privacy of the user's trajectory and improves the practicability of the data.

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.

With the rapid development of portable mobile devices, mobile crowd sensing systems (MCS) have been widely studied. However, the sensing data provided by participants in MCS applications is always unreliable, which affects the service quality of the system, and the truth discovery technology can effectively obtain true values from the data provided by multiple users. At the same time, privacy leaks also restrict users’ enthusiasm for participating in the MCS. Based on this, our paper proposes a secure truth discovery for data aggregation in crowd sensing systems, STDDA, which iteratively calculates user weights and true values to obtain real object data. In order to protect the privacy of data, STDDA divides users into several clusters, and users in the clusters ensure the privacy of data by adding secret random numbers to the perceived data. At the same time, the cluster head node uses the secure sum protocol to obtain the aggregation result of the sense data and uploads it to the server so that the server cannot obtain the sense data and weight of individual users, further ensuring the privacy of the user’s sense data and weight. In addition, using the truth discovery method, STDDA provides corresponding processing mechanisms for users’ dynamic joining and exiting, which enhances the robustness of the system. Experimental results show that STDDA has the characteristics of high accuracy, low communication, and high security.

Existing location-privacy-preserving methods primarily focus on solving the problem of location-privacy preservation in the global space. This not only increases the response time of the location service, it also degrades the data quality. In this paper, a k-anonymity algorithm based on locality-se nsitive hashing is proposed to solve the problem of location-privacy preservation in the subspace. In the proposed algorithm, higher efficiency and higher quality of service are achieved by applying a bottom-up grid-search method. Further, reasonable division is obtained based on locality-sensitive hashing by retaining position characteristics. The results of experiments conducted to evaluate the proposed algorithm indicate that the proposed algorithm provides a smaller anonymous spatial region, higher data quality, and lower time cost than methods with no subspace.