scholarly journals Towards Smart Healthcare: Patient Data Privacy and Security in Sensor-Cloud Infrastructure

2018 ◽  
Vol 2018 ◽  
pp. 1-23 ◽  
Author(s):  
Isma Masood ◽  
Yongli Wang ◽  
Ali Daud ◽  
Naif Radi Aljohani ◽  
Hassan Dawood
Keyword(s):  
Data Privacy ◽  
Research Area ◽  
Research Unit ◽  
Patient Data ◽  
Cloud Infrastructure ◽  
Privacy And Security ◽  
Patient Data Privacy ◽  
Smart Healthcare ◽  
Sensor Cloud ◽  
Theory Research

Nowadays, wireless body area networks (WBANs) systems have adopted cloud computing (CC) technology to overcome limitations such as power, storage, scalability, management, and computing. This amalgamation of WBANs systems and CC technology, as sensor-cloud infrastructure (S-CI), is aiding the healthcare domain through real-time monitoring of patients and the early diagnosis of diseases. Hence, the distributed environment of S-CI presents new threats to patient data privacy and security. In this paper, we review the techniques for patient data privacy and security in S-CI. Existing techniques are classified as multibiometric key generation, pairwise key establishment, hash function, attribute-based encryption, chaotic maps, hybrid encryption, Number Theory Research Unit, Tri-Mode Algorithm, Dynamic Probability Packet Marking, and Priority-Based Data Forwarding techniques, according to their application areas. Their pros and cons are presented in chronological order. We also provide our six-step generic framework for patient physiological parameters (PPPs) privacy and security in S-CI: (1) selecting the preliminaries; (2) selecting the system entities; (3) selecting the technique; (4) accessing PPPs; (5) analysing the security; and (6) estimating performance. Meanwhile, we identify and discuss PPPs utilized as datasets and provide the performance evolution of this research area. Finally, we conclude with the open challenges and future directions for this flourishing research area.

scholarly journals Privacy-Preserving Pandemic Monitoring

2021 ◽  
pp. 194-206
Author(s):  
Thu Yein Win ◽  
Hugo Tianfield
Keyword(s):  
Public Health ◽  
Data Sharing ◽  
Data Privacy ◽  
Privacy Preserving ◽  
Patient Data ◽  
Future Research ◽  
Health Safety ◽  
Patient Data Privacy ◽  
Significant Challenge ◽  
The World

The recent COVID-19 pandemic has presented a significant challenge for health organisations around the world in providing treatment and ensuring public health safety. While this has highlighted the importance of data sharing amongst them, it has also highlighted the importance of ensuring patient data privacy in doing so. This chapter explores the different techniques which facilitate this, along with their overall implementations. It first provides an overview of pandemic monitoring and the privacy implications associated with it. It then explores the different privacy-preserving approaches that have been used in existing research. It also explores the strengths as well as their limitations, along with possible areas for future research.

Survey of Cloud Computing in Healthcare Sector

2017 ◽  
Vol 7 (7) ◽  
pp. 337
Author(s):  
S. Karthiga Devi ◽  
B. Arputhamary
Keyword(s):  
Cloud Computing ◽  
Healthcare Providers ◽  
High Volume ◽  
Healthcare Services ◽  
Healthcare Sector ◽  
Cloud Infrastructure ◽  
Healthcare Organizations ◽  
Privacy And Security ◽  
Healthcare Data ◽  
Number Of Patients

Today the volume of healthcare data generated increased rapidly because of the number of patients in each hospital increasing.  These data are most important for decision making and delivering the best care for patients. Healthcare providers are now faced with collecting, managing, storing and securing huge amounts of sensitive protected health information. As a result, an increasing number of healthcare organizations are turning to cloud based services. Cloud computing offers a viable, secure alternative to premise based healthcare solutions. The infrastructure of Cloud is characterized by a high volume storage and a high throughput. The privacy and security are the two most important concerns in cloud-based healthcare services. Healthcare organization should have electronic medical records in order to use the cloud infrastructure. This paper surveys the challenges of cloud in healthcare and benefits of cloud techniques in health care industries.

scholarly journals Federated Learning in a Medical Context: A Systematic Literature Review

2021 ◽  
Vol 21 (2) ◽  
pp. 1-31
Author(s):  
Bjarne Pfitzner ◽  
Nico Steckhan ◽  
Bert Arnrich
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Data Privacy ◽  
Research Area ◽  
Learning Models ◽  
Related Data ◽  
Private Data ◽  
Large Databases ◽  
Machine Learning Models

Data privacy is a very important issue. Especially in fields like medicine, it is paramount to abide by the existing privacy regulations to preserve patients’ anonymity. However, data is required for research and training machine learning models that could help gain insight into complex correlations or personalised treatments that may otherwise stay undiscovered. Those models generally scale with the amount of data available, but the current situation often prohibits building large databases across sites. So it would be beneficial to be able to combine similar or related data from different sites all over the world while still preserving data privacy. Federated learning has been proposed as a solution for this, because it relies on the sharing of machine learning models, instead of the raw data itself. That means private data never leaves the site or device it was collected on. Federated learning is an emerging research area, and many domains have been identified for the application of those methods. This systematic literature review provides an extensive look at the concept of and research into federated learning and its applicability for confidential healthcare datasets.

Data-driven sector coupling in 5G-based smart networks

2021 ◽  
Vol 22 (1) ◽  
pp. 53-68
Author(s):  
Guenter Knieps
Keyword(s):  
Data Privacy ◽  
Service Providers ◽  
Policy Implications ◽  
Network Industries ◽  
5G Networks ◽  
Privacy And Security ◽  
Open Set ◽  
Application Service ◽  
Downstream Application

5G attains the role of a GPT for an open set of downstream IoT applications in various network industries and within the app economy more generally. Traditionally, sector coupling has been a rather narrow concept focusing on the horizontal synergies of urban system integration in terms of transport, energy, and waste systems, or else the creation of new intermodal markets. The transition toward 5G has fundamentally changed the framing of sector coupling in network industries by underscoring the relevance of differentiating between horizontal and vertical sector coupling. Due to the fixed mobile convergence and the large open set of complementary use cases, 5G has taken on the characteristics of a generalized purpose technology (GPT) in its role as the enabler of a large variety of smart network applications. Due to this vertical relationship, characterized by pervasiveness and innovational complementarities between upstream 5G networks and downstream application sectors, vertical sector coupling between the provider of an upstream GPT and different downstream application industries has acquired particular relevance. In contrast to horizontal sector coupling among different application sectors, the driver of vertical sector coupling is that each of the heterogeneous application sectors requires a critical input from the upstream 5G network provider and combines this with its own downstream technology. Of particular relevance for vertical sector coupling are the innovational complementarities between upstream GPT and downstream application sectors. The focus on vertical sector coupling also has important policy implications. Although the evolution of 5G networks strongly depends on the entrepreneurial, market-driven activities of broadband network operators and application service providers, the future of 5G as a GPT is heavily contingent on the role of frequency management authorities and European regulatory policy with regard to data privacy and security regulations.

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