Hybrid privacy-preserving clinical decision support system in fog–cloud computing

Liu, Ximeng; Deng, Robert H.; Yang, Yang; Tran, Ngoc Hieu; Zhong, Shangping

doi:10.1016/j.future.2017.03.018

Cited by 101 publications

(52 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It is widely used to realize privacypreserving outsourced data computation. Liu et al [70] designed a hybrid clinical decision system in fog-cloud network to monitor patients' physical conditions in real time by combining data mining with Paillier homomorphic encryption. This system achieves lightweight and real-time data processing in fog nodes, while high accuracy disease decision algorithms are implemented in the cloud.…”

Section: B Secure Data Processing In Edge Computingmentioning

confidence: 99%

A Survey on Secure Data Analytics in Edge Computing

Liú

Yan

Ding

et al. 2019

IEEE Internet Things J.

148

103

View full text Add to dashboard Cite

Internet of Things (IoT) is gaining increasing popularity. Overwhelming volumes of data are generated by IoT devices. Those data after analytics provide significant information that could greatly benefit IoT applications. Different from traditional applications, IoT applications such as environmental monitoring, smart navigation and smart healthcare come with new requirements such as mobility, real-time response, and location awareness. However, traditional cloud computing paradigm cannot satisfy these demands due to centralized processing and being far away from local devices. Hence, edge computing was introduced to perform data processing and storage in the edge of networks, which is closer to data sources than cloud computing, thus efficient and location-aware. Unfortunately, edge computing brings new security and privacy challenges when applied to data analytics. The literature still lacks a thorough review on the recent advances in secure data analytics in edge computing. In this paper, we first introduce the concept and features of edge computing, and then propose a number of requirements for its secure data analytics by analyzing potential security threats in edge computing. Furthermore, we give a comprehensive review on the pros and cons of the existing works on data analytics in edge computing based on our proposed requirements. Based on our literature survey, we highlight current open issues and propose future research directions.

show abstract

Section: B Secure Data Processing In Edge Computingmentioning

confidence: 99%

A Survey on Secure Data Analytics in Edge Computing

Liú

Yan

Ding

et al. 2019

IEEE Internet Things J.

148

103

View full text Add to dashboard Cite

show abstract

“…Clinical decision support systems were proposed to reduce human interaction and man-made errors [53]. In an edge/fog computing paradigm, a real-time decision making is possible in AI 1 -enabled devices [54]. However, the interoperability among the devices from diverse vendor-base must be ensured at a real-time, using a framework like MeDIC.…”

Section: ) Inside An Intensive Care Unitmentioning

confidence: 99%

Towards Medical Data Interoperability Through Collaboration of Healthcare Devices

et al. 2020

View full text Add to dashboard Cite

In the era of smart devices and connected neighborhoods, the ubiquitous monitoring and care of patients are possible with the Internet of Medical Things (IoMT). Smart healthcare devices may serve their purpose well when they are able to share patient's data with each other. However, data formats vary widely across vendors, rendering these devices not interoperable. Recent solutions mostly rely on cloud services where a source device uploads the data, and the sink devices download it conforming to their own native formats. However, the quality of service is expected to deteriorate in a cloud processing regime with inherent network delays and traffic congestion, and the real-time data acquisition and manipulation is, therefore, not possible. This article presents MeDIC, a framework of Medical Data Interoperability through Collaboration of healthcare devices. MeDIC improves over a cloud-based IoMT by utilizing translation resources at the network edge, with its probing and translating agents. The probing agents maintain a capability list of MeDIC devices within a local network and enable one MeDIC device to request data conversion from another device when the former is not capable of this conversion by itself. The translating agent of the later then converts the data into the required format and returns it to the former. These novel agents allow IoMT devices to share their redundant computing resources for data translations in order to minimize cloud accesses. Legacy devices are supported through MeDIC-enabled, fog resource managers. We evaluate MeDIC in four use cases with rigorous simulations, which prove that this collaborative framework not only reduces the uplink traffic but also improves the response time, which is critical in real-time medical applications.

show abstract

“…As the data increases day-by-day, the vast amount of data have to be collected, analyzed, managed, and stored, in the cloud which increases the complexity of cloud-based services. To address this issue, we introduce fog computing [25][26][27][28] at the edge of the cloud for pre-processing of generated data. Fog improve the efficiency by reducing the data transfer to the cloud transform from physical devices.…”

Section: Health Information Science and Systemsmentioning

confidence: 99%

A study on medical Internet of Things and Big Data in personalized healthcare system

Jagadeeswari

Subramaniyaswamy

Ravi

et al. 2018

Health Inf Sci Syst

265

View full text Add to dashboard Cite

Personalized healthcare systems deliver e-health services to fulfill the medical and assistive needs of the aging population. Internet of Things (IoT) is a significant advancement in the Big Data era, which supports many real-time engineering applications through enhanced services. Analytics over data streams from IoT has become a source of user data for the healthcare systems to discover new information, predict early detection, and makes decision over the critical situation for the improvement of the quality of life. In this paper, we have made a detailed study on the recent emerging technologies in the personalized healthcare systems with the focus towards cloud computing, fog computing, Big Data analytics, IoT and mobile based applications. We have analyzed the challenges in designing a better healthcare system to make early detection and diagnosis of diseases and discussed the possible solutions while providing e-health services in secure manner. This paper poses a light on the rapidly growing needs of the better healthcare systems in real-time and provides possible future work guidelines.

show abstract

Hybrid privacy-preserving clinical decision support system in fog–cloud computing

Cited by 101 publications

References 20 publications

A Survey on Secure Data Analytics in Edge Computing

A Survey on Secure Data Analytics in Edge Computing

Towards Medical Data Interoperability Through Collaboration of Healthcare Devices

A study on medical Internet of Things and Big Data in personalized healthcare system

Contact Info

Product

Resources

About