A Survey on Healthcare Data: A Security Perspective

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-26
Author(s):  
A. K. Singh ◽  
A. Anand ◽  
Z. Lv ◽  
H. Ko ◽  
A. Mohan
Keyword(s):  
Data Security ◽  
Medical Data ◽  
Future Research ◽  
Healthcare Applications ◽  
Innovative Strategies ◽  
Healthcare Data ◽  
Smart Healthcare ◽  
Healthcare Needs ◽  
Comprehensive Survey ◽  
Health Data Security

With the remarkable development of internet technologies, the popularity of smart healthcare has regularly come to the fore. Smart healthcare uses advanced technologies to transform the traditional medical system in an all-round way, making healthcare more efficient, more convenient, and more personalized. Unfortunately, medical data security is a serious issue in the smart healthcare systems. It becomes a fundamental challenge that requires the development of efficient innovative strategies towards fulfilling the healthcare needs and supporting secure healthcare transfer and delivery. This article provides a comprehensive survey on state-of-the-art techniques for health data security and their new trends for solving challenges in real-world applications. We survey the various notable cryptography, biometrics, watermarking, and blockchain-based security techniques for healthcare applications. A comparative analysis is also performed to identify the contribution of reviewed techniques in terms of their objective, methodology, type of medical data, important features, and limitations. At the end, we discuss the open issues and research directions to explore the promising areas for future research.

scholarly journals eGAP: An Evolutionary Game Theoretic Approach to Random Forest Pruning

2020 ◽  
Vol 4 (4) ◽  
pp. 37
Author(s):  
Khaled Fawagreh ◽  
Mohamed Medhat Gaber
Keyword(s):  
Random Forest ◽  
Replicator Dynamics ◽  
Evolutionary Game ◽  
Theoretic Approach ◽  
Healthcare Applications ◽  
Healthcare Data ◽  
Smart Healthcare ◽  
Game Theoretic ◽  
Game Theoretic Approach

To make healthcare available and easily accessible, the Internet of Things (IoT), which paved the way to the construction of smart cities, marked the birth of many smart applications in numerous areas, including healthcare. As a result, smart healthcare applications have been and are being developed to provide, using mobile and electronic technology, higher diagnosis quality of the diseases, better treatment of the patients, and improved quality of lives. Since smart healthcare applications that are mainly concerned with the prediction of healthcare data (like diseases for example) rely on predictive healthcare data analytics, it is imperative for such predictive healthcare data analytics to be as accurate as possible. In this paper, we will exploit supervised machine learning methods in classification and regression to improve the performance of the traditional Random Forest on healthcare datasets, both in terms of accuracy and classification/regression speed, in order to produce an effective and efficient smart healthcare application, which we have termed eGAP. eGAP uses the evolutionary game theoretic approach replicator dynamics to evolve a Random Forest ensemble. Trees of high resemblance in an initial Random Forest are clustered, and then clusters grow and shrink by adding and removing trees using replicator dynamics, according to the predictive accuracy of each subforest represented by a cluster of trees. All clusters have an initial number of trees that is equal to the number of trees in the smallest cluster. Cluster growth is performed using trees that are not initially sampled. The speed and accuracy of the proposed method have been demonstrated by an experimental study on 10 classification and 10 regression medical datasets.

Opportunistic Edge Computing Architecture for Smart Healthcare Systems

2021 ◽  
pp. 289-306
Author(s):  
Nivethitha V. ◽  
Aghila G.
Keyword(s):  
Cloud Computing ◽  
Healthcare Systems ◽  
Edge Computing ◽  
Healthcare Applications ◽  
Sensitive Data ◽  
Network Resource ◽  
Computing Architecture ◽  
Healthcare Data ◽  
Smart Healthcare ◽  
Network Response Time

Some of the largest global industries that is driving smart city environments are anywhere and anytime health monitoring applications. Smart healthcare systems need to be more preventive and responsive as they deal with sensitive data. Even though cloud computing provides solutions to the smart healthcare applications, the major challenge imposed on cloud computing is how could the centralized traditional cloud computing handle voluminous data. The existing models may encounter problems related to network resource utilization, overheads in network response time, and communication latency. As a solution to these problems, edge-oriented computing has emerged as a new computing paradigm through localized computing. Edge computing expands the compute, storage, and networking capabilities to the edge of the network which will respond to the above-mentioned issues. Based on cloud computing and edge computing, in this chapter an opportunistic edge computing architecture is introduced for smart provisioning of healthcare data.

scholarly journals Privacy-preserving healthcare informatics: a review

2021 ◽  
Vol 36 ◽  
pp. 04005
Author(s):  
Kah Meng Chong
Keyword(s):  
Data Privacy ◽  
Differential Privacy ◽  
State Of The Art ◽  
Future Research ◽  
Sensitive Information ◽  
Research Directions ◽  
Data Anonymization ◽  
Healthcare Data ◽  
Comprehensive Survey ◽  
Future Research Directions

Electronic Health Record (EHR) is the key to an efficient healthcare service delivery system. The publication of healthcare data is highly beneficial to healthcare industries and government institutions to support a variety of medical and census research. However, healthcare data contains sensitive information of patients and the publication of such data could lead to unintended privacy disclosures. In this paper, we present a comprehensive survey of the state-of-the-art privacy-enhancing methods that ensure a secure healthcare data sharing environment. We focus on the recently proposed schemes based on data anonymization and differential privacy approaches in the protection of healthcare data privacy. We highlight the strengths and limitations of the two approaches and discussed some promising future research directions in this area.

scholarly journals Mobile cloud computing in smart healthcare Applications

2021 ◽  
Vol I (I) ◽  
Author(s):  
Kavitha N
Keyword(s):  
Cloud Computing ◽  
Mobile Cloud Computing ◽  
Critical Role ◽  
Cost Effective ◽  
Mobile Cloud ◽  
Healthcare Applications ◽  
Related Data ◽  
Healthcare Data ◽  
Smart Healthcare ◽  
Effective Manner

For urban residents, the Smart Healthcare idea is increasingly prevalent. This service's primary purpose is to provide patients with healthcare data. Using mobile communication and sharing in real time is necessary. Data processing and analysis are all part of this. As a result, cloud computing allows individuals to connect and access healthcare-related data. Data-related tasks like as sharing, processing, and analysis may be offloaded to mobile users for patients via mobile cloud devices, which can play a critical role in mobile cloud computing. Using this service, hospitals and clinics will be able to provide smart healthcare to their patients in a cost-effective manner. The usage of cloud computing will guarantee that patients get high-quality service.

scholarly journals Emerging Wireless Sensor Networks and Internet of Things Technologies—Foundations of Smart Healthcare

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3619 ◽  
Author(s):  
Gordana Gardašević ◽  
Konstantinos Katzis ◽  
Dragana Bajić ◽  
Lazar Berbakov
Keyword(s):  
Internet Of Things ◽  
Energy Efficient ◽  
Secure Communication ◽  
Communication Technologies ◽  
Healthcare Systems ◽  
Healthcare Applications ◽  
Privacy And Security ◽  
Healthcare Data ◽  
Smart Healthcare ◽  
Communication Links

Future smart healthcare systems—often referred to as Internet of Medical Things (IoMT) – will combine a plethora of wireless devices and applications that use wireless communication technologies to enable the exchange of healthcare data. Smart healthcare requires sufficient bandwidth, reliable and secure communication links, energy-efficient operations, and Quality of Service (QoS) support. The integration of Internet of Things (IoT) solutions into healthcare systems can significantly increase intelligence, flexibility, and interoperability. This work provides an extensive survey on emerging IoT communication standards and technologies suitable for smart healthcare applications. A particular emphasis has been given to low-power wireless technologies as a key enabler for energy-efficient IoT-based healthcare systems. Major challenges in privacy and security are also discussed. A particular attention is devoted to crowdsourcing/crowdsensing, envisaged as tools for the rapid collection of massive quantities of medical data. Finally, open research challenges and future perspectives of IoMT are presented.

scholarly journals A Systematic Review of Federated Learning in the Healthcare Area: From the Perspective of Data Properties and Applications

2021 ◽  
Vol 11 (23) ◽  
pp. 11191
Author(s):  
Prayitno ◽  
Chi-Ren Shyu ◽  
Karisma Trinanda Putra ◽  
Hsing-Chung Chen ◽  
Yuan-Yu Tsai ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Data Partitioning ◽  
Data Driven ◽  
Future Research ◽  
Healthcare Applications ◽  
Smart Healthcare ◽  
Protection Mechanisms ◽  
Benchmark Datasets ◽  
Deep Learning Model

Recent advances in deep learning have shown many successful stories in smart healthcare applications with data-driven insight into improving clinical institutions’ quality of care. Excellent deep learning models are heavily data-driven. The more data trained, the more robust and more generalizable the performance of the deep learning model. However, pooling the medical data into centralized storage to train a robust deep learning model faces privacy, ownership, and strict regulation challenges. Federated learning resolves the previous challenges with a shared global deep learning model using a central aggregator server. At the same time, patient data remain with the local party, maintaining data anonymity and security. In this study, first, we provide a comprehensive, up-to-date review of research employing federated learning in healthcare applications. Second, we evaluate a set of recent challenges from a data-centric perspective in federated learning, such as data partitioning characteristics, data distributions, data protection mechanisms, and benchmark datasets. Finally, we point out several potential challenges and future research directions in healthcare applications.

Security in Smart Healthcare System: A Comprehensive Survey

2017 ◽  
Vol 7 (10) ◽  
pp. 39
Author(s):  
P. Jeyadurga ◽  
S. Ebenezer Juliet ◽  
I. Joshua Selwyn ◽  
P. Sivanisha
Keyword(s):  
Secure Communication ◽  
Smart Cities ◽  
Healthcare Systems ◽  
Internet Security ◽  
Security Risk ◽  
System A ◽  
Smart Healthcare ◽  
Comprehensive Survey ◽  
Healthcare Monitoring ◽  
The Internet Of Things

The Internet of things (IoT) is one of the emerging technologies that brought revolution in many application domains such as smart cities, smart retails, healthcare monitoring and so on. As the physical objects are connected via internet, security risk may arise. This paper analyses the existing technologies and protocols that are designed by different authors to ensure the secure communication over internet. It additionally focuses on the advancement in healthcare systems while deploying IoT services.

A Comprehensive Survey of Neural Architecture Search

2021 ◽  
Vol 54 (4) ◽  
pp. 1-34
Author(s):  
Pengzhen Ren ◽  
Yun Xiao ◽  
Xiaojun Chang ◽  
Po-yao Huang ◽  
Zhihui Li ◽  
...  
Keyword(s):  
Research Work ◽  
Search Space ◽  
Feature Representation ◽  
Future Research ◽  
Related Research ◽  
Neural Architecture ◽  
Comprehensive Survey ◽  
Future Research Directions ◽  
Intuitive Idea ◽  
New Perspective

Deep learning has made substantial breakthroughs in many fields due to its powerful automatic representation capabilities. It has been proven that neural architecture design is crucial to the feature representation of data and the final performance. However, the design of the neural architecture heavily relies on the researchers’ prior knowledge and experience. And due to the limitations of humans’ inherent knowledge, it is difficult for people to jump out of their original thinking paradigm and design an optimal model. Therefore, an intuitive idea would be to reduce human intervention as much as possible and let the algorithm automatically design the neural architecture. Neural Architecture Search ( NAS ) is just such a revolutionary algorithm, and the related research work is complicated and rich. Therefore, a comprehensive and systematic survey on the NAS is essential. Previously related surveys have begun to classify existing work mainly based on the key components of NAS: search space, search strategy, and evaluation strategy. While this classification method is more intuitive, it is difficult for readers to grasp the challenges and the landmark work involved. Therefore, in this survey, we provide a new perspective: beginning with an overview of the characteristics of the earliest NAS algorithms, summarizing the problems in these early NAS algorithms, and then providing solutions for subsequent related research work. In addition, we conduct a detailed and comprehensive analysis, comparison, and summary of these works. Finally, we provide some possible future research directions.

Sign in / Sign up

Export Citation Format

Share Document