Abstract
Wireless sensor networks are considered as one of the key research areas in mobile healthcare systems. Those rapidly evolving sensor networks technologies and their deployment in mobile healthcare systems might pose many unseen security and privacy threats. Such threats might affect patient's safety and privacy and confidentiality and integrity of healthcare data, and so forth. This special issue is aimed at fostering the latest development in the design, evaluation, and implementation in the field of mobile healthcare applications of wireless sensor networks. Main research topics of the papers in this special issue include (1) secure and reliable transmission protocols for providing confidentiality, authenticity, integrity, and authenticity of health or medical information, (2) authentication protocols for validating the legitimacy of sensors or tags used by one patient or a group of patients, and (3) review and discussions about security-controlled privacy guarantee for healthcare applications. They are summarized as follows.
The paper “Robust Distributed Reprogramming Protocol of Wireless Sensor Networks for Healthcare Systems” proposes a secure distributed reprogramming protocol to propagate a new code image or relevant commands to sensor nodes for healthcare systems using wireless sensor networks. This protocol can ensure authenticity, integrity of program image, and node compromised tolerance.
The paper “Watermarking of Parkinson Disease Speech in Cloud-Based Healthcare Framework” proposes a cloud-based healthcare framework that will authenticate speech data from a patient suspected to have Parkinson's disease. The speech signals of patient are recorded by a smartphone and transmitted to the cloud via Internet. The authors propose a discrete wavelet transform-singular value decomposition based speech watermarking module to embed watermark to the signal for data authenticity. This method is proved to be robust against attacks such as additive white Gaussian noise and filtering.
The paper “Tracking and Repairing Damaged Healthcare Databases Using the Matrix” proposes an efficient damage assessment and recovery algorithm to recover the database from malicious transactions for securing health information from malicious attacks. The proposed protocol is based on data dependency, not transaction dependency, and uses a single matrix for identifying the valid data items of healthcare databases during the damage assessment process. As compared with traditional methods which require scanning the entire log from the point of attack to the end, it saves memory and reduces recovery time which reduces denial of service of the healthcare database.
The paper “Fast Antinoise RFID-Aided Medical Care System” proposes a grouping proof scheme to help the nursing staff on their final check automatically before a medicine round. It can generate multiple proofs for each patient and help the medical caretakers follow the five-right policy to give correct drugs to their patients. The authors give the proof that their proposed scheme is reliable, resists most security threats, and ensures the integrity of the proof. Moreover, it can protect patients’ sensitive information and location privacy to guarantee patients’ anonymity on the RFID tags.
The paper “Novel Authentication Schemes for IoT Based Healthcare Systems” proposes a sensor (or sensor tags) based communication architecture for future IoT based healthcare service systems. The authors also propose a secure single sign-on authentication scheme and a robust coexistence proof protocol for IoT based healthcare systems. The authors give a formal security analysis to prove that the robustness of the two proposed schemes is guaranteed under the adversary model. These two schemes can provide robust entity authentication and secure data communication among sensors used in healthcare systems.
The paper “A Review of Differential Privacy in Individual Data Release” reviews and classified the literatures published in recent years about the methods of differentially private data releasing. The reviewed methods are based on histograms, tree structures, time-series, graphs, and frequent pattern mining data release methods. The authors summarize a series of data release methods for privacy problem and provide certain references for differential privacy in further study.