Privacy Protection for Blockchain-Based Healthcare IoT Systems: A Survey

To enable precision medicine and remote patient monitoring,internet of healthcare things(IoHT)has gained significant interest as a promising technique.With the widespread use of IoHT,nonetheless,privacy infringements such as IoHT data leakage have raised serious public concerns.On the other side,blockchain and distributed ledger technologies have demonstrated great potential for enhancing trustworthiness and privacy protection for IoHT systems.In this survey,a holistic review of existing blockchain-based IoHT systems is conducted to indicate the feasibility of combining blockchain and IoHT in privacy protection.In addition,various types of privacy challenges in IoHT are identified by examining general data protection regulation(GDPR).More importantly,an associated study of cutting-edge privacy-preserving techniques for the identified IoHT privacy challenges is presented.Finally,several challenges in four promising research areas for blockchain-based IoHT systems are pointed out,with the intent of motivating researchers working in these fields to develop possible solutions.

Self-Assembled Porous-Reinforcement Microstructure-Based Flexible Triboelectric Patch for Remote Healthcare

Realizing real-time monitoring of physiological signals is vital for preventing and treating chronic diseases in elderly individuals. However,wearable sensors with low power consumption and high sensitivity to both weak physiological signals and large mechanical stimuli remain challenges.Here, a flexible triboelectric patch(FTEP) based on porous-reinforcement microstructures for remote health monitoring has been reported. The porousreinforcement microstructure is constructed by the self-assembly of silicone rubber adhering to the porous framework of the PU sponge. The mechanical properties of the FTEP can be regulated by the concentrations of silicone rubber dilution. For pressure sensing, its sensitivity can be effectively improved fivefold compared to the device with a solid dielectric layer, reaching 5.93 kPa^(-1) under a pressure range of 0–5 kPa. In addition, the FTEP has a wide detection range up to 50 kPa with a sensitivity of 0.21 kPa^(-1). The porous microstructure makes the FTEP ultra-sensitive to external pressure, and the reinforcements endow the device with a greater deformation limit in a wide detection range. Finally, a novel concept of the wearable Internet of Healthcare(Io H) system for real-time physiological signal monitoring has been proposed, which could provide real-time physiological information for ambulatory personalized healthcare monitoring.

互联网医疗在我国新型冠状病毒肺炎疫情防控中的探索与实践

从控制传染源、切断传播途径、保护易感人群3个关键环节分析互联网医疗在此次疫情防控中的应用价值和典型场景:基于大数据和云计算技术实施主动监测,识别预警疑似病例;互联网医院简化服务流程,开展远程会诊和药事服务,减少人员聚集交叉感染;基于云平台实现门诊全流程自助服务和病历无纸化管理。梳理我国互联网医疗建设过程中遇到的瓶颈并提出发展建议,为突发公共卫生事件应急响应信息化建设提供参考。

τSQWRL:A TSQL2-Like Query Language for Temporal Ontologies Generated from JSON Big Data

Temporal ontologies allow to represent not only concepts,their properties,and their relationships,but also time-varying information through explicit versioning of definitions or through the four-dimensional perdurantist view.They are widely used to formally represent temporal data semantics in several applications belonging to different fields(e.g.,Semantic Web,expert systems,knowledge bases,big data,and artificial intelligence).They facilitate temporal knowledge representation and discovery,with the support of temporal data querying and reasoning.However,there is no standard or consensual temporal ontology query language.In a previous work,we have proposed an approach namedτJOWL(temporal OWL 2 from temporal JSON,where OWL 2 stands for"OWL 2 Web Ontology Language"and JSON stands for"JavaScript Object Notation").τJOWL allows(1)to automatically build a temporal OWL 2 ontology of data,following the Closed World Assumption(CWA),from temporal JSON-based big data,and(2)to manage its incremental maintenance accommodating their evolution,in a temporal and multi-schema-version environment.In this paper,we propose a temporal ontology query language forτJOWL,namedτSQWRL(temporal SQWRL),designed as a temporal extension of the ontology query language—Semantic Query-enhanced Web Rule Language(SQWRL).The new language has been inspired by the features of the consensual temporal query language TSQL2(Temporal SQL2),well known in the temporal(relational)database community.The aim of the proposal is to enable and simplify the task of retrieving any desired ontology version or of specifying any(complex)temporal query on time-varying ontologies generated from time-varying big data.Some examples,in the Internet of Healthcare Things(IoHT)domain,are provided to motivate and illustrate our proposal.

Public auditing for real‑time medical sensor data in cloud‑assisted HealthIIoT system

With the advancement of industrial internet of things(IIoT),wireless medical sensor networks(WMSNs)have been widely introduced in modern healthcare systems to collect real-time medical data from patients,which is known as HealthIIoT.Considering the limited computing and storage capabilities of lightweight HealthIIoT devices,it is necessary to upload these data to remote cloud servers for storage and maintenance.However,there are still some serious security issues within outsourcing medical sensor data to the cloud.One of the most signifcant challenges is how to ensure the integrity of these data,which is a prerequisite for providing precise medical diagnosis and treatment.To meet this challenge,we propose a novel and efcient public auditing scheme,which is suitable for cloud-assisted HealthIIoT system.Specifcally,to address the contradiction between the high real-time requirement of medical sensor data and the limited computing power of HealthIIoT devices,a new online/ofine tag generation algorithm is designed to improve preprocessing efciency;to protect medical data privacy,a secure hash function is employed to blind the data proof.We formally prove the security of the presented scheme,and evaluate the performance through detailed experimental comparisons with the state-of-the-art ones.The results show that the presented scheme can greatly improve the efciency of tag generation,while achieving better auditing performance than previous schemes.