Body Sensor Network Processing Mechanism for Micro-Data Security Publishing

BackgroundPatient privacy protection is a fundamental ethical obligation in healthcare, particularly in operating rooms where anesthesia and complex surgical procedures increase patients’ vulnerability. Mental workload and moral sensitivity have been identified as key factors influencing ethical behavior, yet their interrelationships in the context of privacy protection among operating room nurses remain unclear.ObjectivesTo investigate the associations between mental workload, moral sensitivity, and patient privacy protection among operating room nurses in China, and to examine the mediating role of moral sensitivity in this relationship.MethodA cross-sectional study was conducted from May to July 2025 in tertiary hospitals across Chongqing, Beijing, Harbin, and Sichuan Province. Using convenience sampling, a total of 573 valid responses from operating room nurses were analyzed using the Patient Privacy Scale, the Moral Sensitivity Questionnaire-Revised Chinese Version, and the NASA Task Load Index. Pearson correlation and mediation analyses were performed using SPSS 26.0 with PROCESS macro (Model 4), applying bootstrapping with 5,000 resamples to estimate indirect effects.ResultsThe mean scores for patient privacy protection, moral sensitivity, and mental workload were 82.384 ± 5.169, 45.775 ± 6.053, and 45.450 ± 14.826, respectively. Mental workload was negatively correlated with both patient privacy protection (r = −0.655, p < 0.001) and moral sensitivity (r = −0.575, p < 0.001), while moral sensitivity was positively correlated with patient privacy protection (r = 0.506, p < 0.001). Mediation analysis indicated that moral sensitivity partially mediated the association between mental workload and privacy protection, accounting for 14.98% of the total effect.ConclusionGreater mental workload is associated with lower moral sensitivity and lower patient privacy protection among operating room nurses. Moral sensitivity partially mediates this relationship, underscoring the need for interventions aimed at alleviating workload and strengthening moral sensitivity to enhance privacy protection practices.

Nowadays, the design and construction of efficient internet of things (IoTs) has become a new strategies for improving living quality of all aspects. Emerging as one of the most significant extension of medical IoTs, wireless body area networks (WBANs) is capable of monitoring crucial physiological and behavioral information through wearable sensors, offering a new paradigm for the next-generation healthcare systems. As a matter of fact, due to the inherent open wireless communicating characteristics, data security and user privacy issues of WBANs have attracted attentions from both industry and academia. So far, lots of relevant researches emphasize on secure transmission and privacy protection. However, the computation and communication limitations for individual WBAN sensor have not been taken proper consideration. Moreover, the implementation of cloud computing infrastructure has provided WBANs with superior transmission and processing qualities. Emphasizing on the above issues, this paper construct a pairing-free authentication and sensor cooperation scheme in cloud-assisted WBANs, where most of the practical requirements for WBAN sensors could be satisfied. Our design guarantee the sensor anonymity in the whole transmission session. Note that our design offers pairing-free validation procedure followed with active sensor cooperation, which is suitable for massive sensor scenarios. The security analysis proves that our designed scheme is capable of achieving desired security properties and offer adequate resistances to the charted malicious attacks. Meanwhile, security comparison demonstrates that the proposed protocol is secure compared with the state-of-the-arts.

Wireless Body Area Network (WBAN) is a pioneer trend in healthcare technology.A WBAN consists of small sensors that may be worn or implanted on the patient's body or around them.These sensors are responsible for sending wireless, real-time physiological signs of the patient's body (such as blood pressure, heart rate, blood sugar, temperature, breathing, etc.) to an intermediate device.An intermediate device (such as a mobile phone) collects and prepares data to send and display to the doctor.In the WBAN, since data is sent through all broadcasts, any cyber attack such as eavesdropping and data modification/ sabotage can be done.Any cyber-attack on a WBAN could jeopardize the patient's health; therefore, securing the WBAN plays a crucial role in healthcare applications.Due to the limitation of energy, memory, and processing power of sensors in WBAN, it is not possible to use traditional security methods, such as encryption and security protocols.For this reason, one of the methods to provid security in the WBAN is the use of intrusion detection systems.An intrusion detection system (IDS), as a second-line defense, is one of the security methods in computer networks.Intrusion detection is the intelligent monitoring of network or computer systems to find any security breach.These systems are divided into two groups, signature-based and anomaly-based.One of the advantages 1 55 160 of anomaly-based systems is the detection of zero-day attacks.Statistical, knowledge-based, data mining and machine learning methods can be used to implement anomaly-based intrusion detection systems.In this paper, a new IDS has been presented which is able to detect denial of service (DoS) attacks in a WBAN.The proposed IDS is a hybrid anomaly-based system using machine learning and feature engineering methods.In the proposed IDS, a genetic algorithm is used to select features of collected data, in a way that increases the performance of the IDS, and as a result the WBAN (increasing detection rate and reducing energy consumption in the node).Then, using support vector machine and k nearest neighbor algorithms, the data classification is performed to detect DoS traffic from regular data traffic.Simulation results indicate that the proposed IDS has effective performance with a 90% detection rate.

Wireless Body Area Networks (WBANs) are integral to modern healthcare systems, providing continuous health monitoring and real-time data transmission. The sensitivity of medical data being transmitted makes security a significant concern in WBANs. This study explores the application of the Elliptic Curve Cryptography (ECC)-based Diffie-Hellman Key Exchange (ECDH) algorithm to enhance security within WBANs. The study investigates the suitability of ECC for this context and evaluates the performance and security implications of implementing ECDH in WBANs. The findings reveal that ECDH provides a robust and computationally efficient solution for secure key exchange in WBANs, addressing inherent vulnerabilities. The adoption of ECC-based ECDH is poised to bolster data confidentiality and integrity in WBANs, promoting trust and widespread use of these networks in healthcare applications. This research contributes to the growing body of knowledge regarding security measures in WBANs and opens new avenues for the secure transmission of sensitive medical information.

The recent advancement in wireless body area networks (WBAN) plays an important role in remote health care systems. However, these networks are suffering from data security and privacy threats. Lack of anonymous authentication and secure data communication leads to operation failure in WBAN. Computational cost and privacy preservation are the two major hindrances for anonymous authentication in many existing schemes. Therefore, a secure and efficient privacy-preserving anonymous authentication scheme is proposed to provide data security and privacy to the users with low computational and communication cost. The performance analysis and experimental simulation results ensure that our proposed anonymous authentication method outperforms the existing systems in terms of providing data security and privacy with less computational overhead.

Recently, Body Sensor Networks (BSNs) are being deployed for monitoring and managing medical conditions as well as human performance in sports. These BSNs include various sensors such as accelerometers, gyroscopes, EMG (Electromyogram), EKG (Electro-cardiograms), and other sensors depending on the needs of the medical conditions. Data from these sensors are typically Time Series data and the data from multiple sensors form multiple, multidimensional time series data. This tutorial describes the technologies that go behind BSNs -- both in terms of the hardware infrastructure as well as the basic software. First, we outline the BSN hardware features and the related requirements. We then discuss the energy and communication choices for BSNs. Next, we discuss approaches for classification, data mining, visualization, and securing these data. We also show several demonstrations of body sensor networks as well as the software that aid in analyzing the data.

Wireless body area networks (WBANs) are an emerging technology expected to revolutionize mobile healthcare via the real-time monitoring and analysis of medical data. This article describes the generalized WBAN concept and examples of WBAN medical applications for a variety of users and scenarios. The properties of wireless communication channels used in WBANs - specifically the human body communications channel and the 60 GHz Millimeter-wave (mmWave) - highlight the challenges facing modern WBAN development. Several research topics related to WBAN implementation in 5G networks are examined, including simulating extremely high-frequency communications, routing protocols, energy harvesting, tailoring WBANs to individual users, and user satisfaction. Future WBAN developments related to 6G networks and quantum technologies are also explored to demonstrate the potential future of mobile health.

Wireless Body Area Networks (WBANs) are critical components of emerging smart healthcare solutions. Due to the intrinsic nature of wireless communication and the potentially sensitive medical data they carry, such networks are often very vulnerable to many types of security threats. As a result, the security of WBANs becomes a prime concern when they are considered for use in healthcare or medical applications. This text enumerates common security threats that a typical WBAN should prepare for. It also provides a set of enhancement mechanisms and techniques that could be implemented to augment the security of WBANs. Furthermore, a vital precautionary activity that should be taken in the initial steps towards developing a secure WBAN system— threat modeling—is also described. This text presents two elaborated example cases for the threat modeling technique for WBANs: patrolling and insulin injection WBANs. The cases demonstrate that threats in WBANs are further sources of vulnerabilities, beyond the definition of threats from common knowledge, that should be evaluated and addressed before deployment. Results show that there are plenty of possible problems that threaten the security and privacy of WBAN systems, which should be taken into strong consideration. It is believed that the proposed threat modeling methodology here will significantly contribute to secure designs of WBANs and can also potentially be generalized to other embedded systems as well.

Wireless Body Area Networks (WBANs), low power, and short-range wireless communication in a near-body area provide advantages, particularly in the medical and healthcare sector: (i) they enable continuous monitoring of patients and (ii) the recording and correlation of physical and biological information. Along with the utilization and integration of these (sensitive) private and personal data, there are substantial requirements concerning security and privacy, as well as protection during processing and transmission. Contrary to the star topology frequently used in various standards, the overall concept of a novel low-data rate token-based WBAN framework is proposed. This work further comprises the evaluation of strategies for handling medical data with WBANs and emphasizes the importance and necessity of encryption and security strategies in the context of sensitive information. Furthermore, this work considers the recent advancements in Artificial Intelligence (AI), which are opening up opportunities for enhancing cyber resilience, but on the other hand, also new attack vectors. Moreover, the implications of targeted regulatory measures, such as the European AI Act, are considered. In contrast to, for instance, the proposed star network topologies of the IEEE 802.15.6 WBAN standard or the Technical Committee (TC) SmartBAN of the European Telecommunication Standards Institute (ETSI), the concept of a ring topology is proposed which concatenates information in the form of a ‘data train’ and thus results in faster and more efficient communication. Beyond that, the conductivity of human skin is included in the approach presented to incorporate a supplementary channel. This direct contact requirement not only fortifies the security of the system but also facilitates a reliable means of secure communication, pivotal in maintaining the integrity of sensitive health data. The work identifies different threat models associated with the WBAN system and evaluates potential data vulnerabilities and risks to maximize security. It highlights the crucial balance between security and efficiency in WBANs, using the token-based approach as a case study. Further, it sets a foundation for future healthcare technology advancements, aiming to ensure the secure and efficient integration of patient data.

With the pervasiveness of the Internet of Things (IoT) and the rapid progress of wireless communications, Wireless Body Area Networks (WBANs) have attracted significant interest from the research community in recent years. As a promising networking paradigm, it is adopted to improve the healthcare services and create a highly reliable ubiquitous healthcare system. However, the flourish of WBANs still faces many challenges related to security and privacy preserving. In such pervasive environment where the context conditions dynamically and frequently change, context-aware solutions are needed to satisfy the users' changing needs. Therefore, it is essential to design an adaptive access control scheme that can simultaneously authorize and authenticate users while considering the dynamic context changes. In this paper, we propose a context-aware access control and anonymous authentication approach based on a secure and efficient Hybrid Certificateless Signcryption (H-CLSC) scheme. The proposed scheme combines the merits of Ciphertext-Policy Attribute-Based Signcryption (CP-ABSC) and Identity-Based Broadcast Signcryption (IBBSC) in order to satisfy the security requirements and provide an adaptive contextual privacy. From a security perspective, it achieves confidentiality, integrity, anonymity, context-aware privacy, public verifiability, and ciphertext authenticity. Moreover, the key escrow and public key certificate problems are solved through this mechanism. Performance analysis demonstrates the efficiency and the effectiveness of the proposed scheme compared to benchmark schemes in terms of functional security, storage, communication and computational cost.

Objective: To review different key agreement schemes which are used to establish Wireless body area network (WBAN) on the basis of identified attacks and also evaluate the schemes on performance parameters. Method: In this paper, an overview of WBAN, its architecture, difference between WSN and WBAN, IEEE 802.15.6 specifications of WBAN, attacks in the environment and security essentials are discussed at first. It further divides the key agreement schemes into four classes and provides an extensive review of the schemes on the basis of distinct parameters viz. data confidentiality, node authentication, data integrity, mutual authentication, unforgeability, unlinkability, forward/backward secrecy, scalability, freshness, dos attack and node capture attack. Schemes are searched using the keywords- (“Traditional Key Agreement Scheme” OR “Physiological Key Agreement Scheme” OR “Signal Based Key Agreement Scheme” OR “Hybrid Key Agreement Scheme” OR “Security in Key Agreement Scheme”) AND (“Wireless Body Area Network” OR “WBAN” OR “Body Area Network” OR “BAN” OR “Body Sensor Network” OR “BSN” OR “Medical Body Area Network” OR “MBAN”). The papers are shortlisted around long stretches of 2003-2021 with a focus on recent work from IEEE Xplore, Springer, Science Direct, ACM, MDPI and Google Scholar databases. Schemes are analyzed against the mentioned attacks and the result of the comparative analysis is shown using tables and chart tools graphically. Findings: The wide coverage of the schemes in this review provides in-depth exposure to the shortcomings of the different schemes against the listed attacks which will provide a road map to the researchers to develop secure schemes in the future. Moreover, maximum schemes do not consider all the three aspects of performance viz. memory efficiency, computational efficiency and energy efficiency which are the foremost parameters in resource scarce environment of WBAN. Novelty: This review is unique as it analyzes the distinct key agreement schemes under specific attacks found in the literature while other review papers discuss the general aspects of the security threats and corresponding counter measures in WBAN environment. It also provides the performance analysis of the key management schemes which are missing from other review works. Keywords: WBAN; IOT; Bio sensors; Security; Privacy; Attacks; Encryption; Key agreement schemes; IEEE 802.15.6

With the quick development of body sensor networks (BSNs), BSNs data are increasing exponentially. At present, the data is stored in a disorder way, thereby leading to difficult to utilize. To make them be used expediently, this paper presents a body-oriented cloud storage model. This paper firstly designs some metadata which contain BSNs contexts, time, sensor position, sensor type and health state, to describe the physiological data in BSNs. Then, the physiological data are encapsulated with the metadata in a coordinator of BSNs. At last, all of them are stored in repositories based on the body-oriented cloud storage model. We experimentally compare the performance of the proposed model to that of traditional one. The results show it can reduce the storage space and make BSNs data utilized expediently.

Wireless body area networks become a new research issue in recent years. In this paper, we propose hibernation and distance estimation schemes for wireless USB (WUSB) over wireless body area networks (WBAN) hierarchical protocol for body sensor networks (BSNs). The proposed middleware platform is composed of hibernation and optimal short distance estimation solution. Firstly, this paper provides WUSB private channel allocation scheme at the m-periodic allocation hibernation of a WBAN superframe. Secondly, three typical location estimation schemes of time difference of arrival (TDoA), frequency difference of arrival (FDoA), and angle of arrival (AoA) are analyzed through comparison of experiment results to propose optimal short distance estimation solution which is executed at each body sensor node on the basis of WUSB over WBAN protocol. And the WBAN host interprets status and motion of the body-sensor objects. The hybrid TDoA/FDoA/AoA algorithm based on UWB ranging shows best performances among the other hybrid schemes for wireless BSNs.

Wireless Body Area Network (WBAN) is a collection of wireless sensor nodes which can be placed within the body or outside the body of a human or a living person which in result observes or monitors the functionality and adjoining situations of the body. Utilizing a Wireless Body Area Network, the patient encounters a greater physical versatility and is never again constrained to remain in the hospital. As the Wireless Body Area Network sensor devices is being utilized for gathering the sensitive data and possibly will run into antagonistic situations, they require complicated and very secure security medium or structure to avoid the vitriolic communications within the system. These devices represent various security and privacy protection of touchy and private patient medical data. The medical records of patients are a significant and an unsolved situation due to which a changing or exploitation to the system is possible. In this research paper, we first present an overview of WBAN, how they utilized for healthcare monitoring, its architecture then highlight major security and privacy requirements and assaults at different network layer in a WBAN and we finally talk about various cryptographic algorithms and laws for providing solution of security and privacy in WBAN.

The trends of data mining on healthcare data for improving medical services have increased because of the electronic healthcare record(EHR) system, which collects a massive amount of data on a daily basis. In the current scenario, hospital maintains its EHR system and stores the detailed information of patients. Data mining for healthcare improvement requires the data from all the EHR systems located at a different location to be stored at the central data mining server. Collection of healthcare data at some untrusted central data mining server raises privacy threats. Healthcare data contains patients’ private information and sharing this information for data mining creates privacy issues. Most of the previous research either focused on k-anonymity technique which causes information loss and decreases data mining accuracy or privacy preserving data mining which is focused on only specific data mining technique. We adopt source anonymous technique as privacy preserving scheme and present a novel scheme for healthcare data collection and mining in this paper. Our scheme collects data from all EHR systems without any information loss and stores at a single central data mining server, also ensuring privacy is preserved. Central data mining server helps to analyze the collected data with different data mining techniques (Association rule mining, Classification, Clustering, etc.) without the involvement of EHR systems. Our scheme is collusion resilient against central data mining server and EHR systems. Theoretical and experimental analysis show the efficiency of our scheme in terms of computation and communication cost. The experimental results using Heart disease dataset show the advantage to EHR systems using the proposed approach in terms of disease prediction accuracy.