scholarly journals SCOR: A secure international informatics infrastructure to investigate COVID-19

2020 ◽  
Vol 27 (11) ◽  
pp. 1721-1726 ◽  
Author(s):  
J L Raisaro ◽  
Francesco Marino ◽  
Juan Troncoso-Pastoriza ◽  
Raphaelle Beau-Lejdstrom ◽  
Riccardo Bellazzi ◽  
...  
Keyword(s):  
Health Informatics ◽  
Treatment Options ◽  
Large Data ◽  
Machine Learning Algorithms ◽  
Privacy And Security ◽  
Healthcare Data ◽  
Security Technologies ◽  
International Scale ◽  
Informatics Infrastructure ◽  
Joint Mission

Abstract Global pandemics call for large and diverse healthcare data to study various risk factors, treatment options, and disease progression patterns. Despite the enormous efforts of many large data consortium initiatives, scientific community still lacks a secure and privacy-preserving infrastructure to support auditable data sharing and facilitate automated and legally compliant federated analysis on an international scale. Existing health informatics systems do not incorporate the latest progress in modern security and federated machine learning algorithms, which are poised to offer solutions. An international group of passionate researchers came together with a joint mission to solve the problem with our finest models and tools. The SCOR Consortium has developed a ready-to-deploy secure infrastructure using world-class privacy and security technologies to reconcile the privacy/utility conflicts. We hope our effort will make a change and accelerate research in future pandemics with broad and diverse samples on an international scale.

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.

User password repetitive patterns analysis and visualization

2016 ◽  
Vol 24 (1) ◽  
pp. 93-115 ◽  
Author(s):  
Xiaoying Yu ◽  
Qi Liao
Keyword(s):  
Security Policy ◽  
Large Data ◽  
Efficient Algorithms ◽  
Privacy And Security ◽  
Data Set ◽  
Web Based ◽  
Content Type ◽  
Word Cloud ◽  
Individual Privacy ◽  
Password Security

Purpose – Passwords have been designed to protect individual privacy and security and widely used in almost every area of our life. The strength of passwords is therefore critical to the security of our systems. However, due to the explosion of user accounts and increasing complexity of password rules, users are struggling to find ways to make up sufficiently secure yet easy-to-remember passwords. This paper aims to investigate whether there are repetitive patterns when users choose passwords and how such behaviors may affect us to rethink password security policy. Design/methodology/approach – The authors develop a model to formalize the password repetitive problem and design efficient algorithms to analyze the repeat patterns. To help security practitioners to analyze patterns, the authors design and implement a lightweight, Web-based visualization tool for interactive exploration of password data. Findings – Through case studies on a real-world leaked password data set, the authors demonstrate how the tool can be used to identify various interesting patterns, e.g. shorter substrings of the same type used to make up longer strings, which are then repeated to make up the final passwords, suggesting that the length requirement of password policy does not necessarily increase security. Originality/value – The contributions of this study are two-fold. First, the authors formalize the problem of password repetitive patterns by considering both short and long substrings and in both directions, which have not yet been considered in past. Efficient algorithms are developed and implemented that can analyze various repeat patterns quickly even in large data set. Second, the authors design and implement four novel visualization views that are particularly useful for exploration of password repeat patterns, i.e. the character frequency charts view, the short repeat heatmap view, the long repeat parallel coordinates view and the repeat word cloud view.

A review on Privacy-Preserving Data Preprocessing

2020 ◽  
pp. 16-30
Author(s):  
Mukesh Soni ◽  
◽  
◽  
◽  
YashKumar Barot ◽  
...  
Keyword(s):  
Health Care ◽  
Information Security ◽  
Social Insurance ◽  
Disease Outbreaks ◽  
Security And Privacy ◽  
Health Care Information ◽  
Privacy And Security ◽  
Healthcare Data ◽  
Security Issues ◽  
Care Information

Health care information has great potential for improving the health care system and also providing fast and accurate outcomes for patients, predicting disease outbreaks, gaining valuable information for prediction in future, preventing such diseases, reducing healthcare costs, and improving overall health. In any case, deciding the genuine utilization of information while saving the patient's identity protection is an overwhelming task. Regardless of the amount of medical data it can help advance clinical science and it is essential to the accomplishment of all medicinal services associations, at the end information security is vital. To guarantee safe and solid information security and cloud-based conditions, It is critical to consider the constraints of existing arrangements and systems for the social insurance of information security and assurance. Here we talk about the security and privacy challenges of high-quality important data as it is used mainly by the healthcare structure and similar industry to examine how privacy and security issues occur when there is a large amount of healthcare information to protect from all possible threats. We will discuss ways that these can be addressed. The main focus will be on recently analyzed and optimized methods based on anonymity and encryption, and we will compare their strengths and limitations, and this chapter closes at last the privacy and security recommendations for best practices for privacy of preprocessing healthcare data.

Materialities between security and privacy: A constructivist account of airport security scanners

2015 ◽  
Vol 46 (4) ◽  
pp. 326-344 ◽  
Author(s):  
Govert Valkenburg ◽  
Irma van der Ploeg
Keyword(s):  
Social Inequalities ◽  
Security And Privacy ◽  
Airport Security ◽  
Privacy And Security ◽  
Security Technologies ◽  
Policy Choices ◽  
Security Regime ◽  
Security Problem ◽  
Technical Issues ◽  
The One

What concepts such as ‘security’ and ‘privacy’ mean in practice is not merely a matter of policy choices or value concepts, but is inherently tied up with the socio-material and technological arrangement of the practices in which they come to matter. In this article, one trajectory in the implementation of a security regime into the sociotechnical arrangement of airport security checking is reconstructed. During this trajectory, gradual modifications or ‘translations’ are performed on what are initially defined as the privacy and security problems. The notion of translation is used to capture the modifications that concepts undergo between different stages of the process: the initial security problem shifts, transforms and comes to be aligned with several other interests and values. We articulate how such translations take place in the material realm, where seemingly technical and natural-scientific givens take part in the negotiations. On the one hand, these negotiations may produce technologies that perform social inequalities. On the other hand, it is in this material realm that translations of problem definitions appear as simply technical issues, exempted from democratic governance. The forms of privacy and security that emerge in the end are thus specific versions with specific social effects, which do not follow in an obvious way from the generic, initial concepts. By focusing on problem definitions and their translations at various stages of the development, we explain how it is possible for potentially stigmatizing and privacy-encroaching effects to occur, even though the security technologies were introduced exactly to preclude those effects.

scholarly journals Significant Impact of Improved Machine Learning Algorithm in The Processes of Large Data Sets

2020 ◽  
pp. 458-467
Author(s):  
Virendra Tiwari ◽  
Balendra Garg ◽  
Uday Prakash Sharma
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Dynamic Environment ◽  
Large Data ◽  
Machine Learning Algorithms ◽  
Streaming Data ◽  
Machine Learning Techniques ◽  
Machine Learning Algorithm ◽  
Learning Mechanisms

The machine learning algorithms are capable of managing multi-dimensional data under the dynamic environment. Despite its so many vital features, there are some challenges to overcome. The machine learning algorithms still requires some additional mechanisms or procedures for predicting a large number of new classes with managing privacy. The deficiencies show the reliable use of a machine learning algorithm relies on human experts because raw data may complicate the learning process which may generate inaccurate results. So the interpretation of outcomes with expertise in machine learning mechanisms is a significant challenge in the machine learning algorithm. The machine learning technique suffers from the issue of high dimensionality, adaptability, distributed computing, scalability, the streaming data, and the duplicity. The main issue of the machine learning algorithm is found its vulnerability to manage errors. Furthermore, machine learning techniques are also found to lack variability. This paper studies how can be reduced the computational complexity of machine learning algorithms by finding how to make predictions using an improved algorithm.

Privacy and Security in e-Health Applications

2012 ◽  
pp. 1141-1166
Author(s):  
Milan Petkovic ◽  
Luan Ibraimi
Keyword(s):  
Digital Technologies ◽  
Health Data ◽  
Security Requirements ◽  
Security And Privacy ◽  
Sensitive Information ◽  
Privacy And Security ◽  
Security Technologies ◽  
Technological Means ◽  
The Individual ◽  
Health Applications

The introduction of e-Health and extramural applications in the personal healthcare domain has raised serious concerns about security and privacy of health data. Novel digital technologies require other security approaches in addition to the traditional “purely physical” approach. Furthermore, privacy is becoming an increasing concern in domains that deal with sensitive information such as healthcare, which cannot absorb the costs of security abuses in the system. Once sensitive information about an individual’s health is uncovered and social damage is done, there is no way to revoke the information or to restitute the individual. Therefore, in addition to legal means, it is very important to provide and enforce privacy and security in healthcare by technological means. In this chapter, the authors analyze privacy and security requirements in healthcare, explain their importance and review both classical and novel security technologies that could fulfill these requirements.

Users as Prosumers of PETs

2014 ◽  
pp. 178-199
Author(s):  
Julio Angulo
Keyword(s):  
Personal Information ◽  
User Involvement ◽  
The Internet ◽  
Data Handling ◽  
Privacy And Security ◽  
Security Technologies ◽  
Frequent Contact ◽  
Internet Users ◽  
Privacy Enhancing Technologies ◽  
Handling Practices

Frequent contact with online businesses requires Internet users to distribute large amounts of personal information. This spreading of users’ information through different Websites can eventually lead to increased probabilities for identity theft, profiling and linkability attacks, as well as other harmful consequences. Methods and tools for securing people’s online activities and protecting their privacy on the Internet, called Privacy Enhancing Technologies (PETs), are being designed and developed. However, these technologies are often perceived as complicated and obtrusive by users who are not privacy aware or are not computer or technology savvy. This chapter explores the way in which users’ involvement has been considered during the development process of PETs and argues that more democratic approaches of user involvement and data handling practices are needed. It advocates towards an approach in which people are not only seen as consumers of privacy and security technologies, but where they can play a role as the producers of ideas and sources of inspiration for the development of usable PETs that meet their actual privacy needs and concerns.

Deep Learning Approaches for Sentiment Analysis Challenges and Future Issues

2022 ◽  
pp. 27-50
Author(s):  
Rajalaxmi Prabhu B. ◽  
Seema S.
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Model Building ◽  
Large Data ◽  
Machine Learning Algorithms ◽  
Large Data Sets ◽  
Data Sets ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Important Challenge

A lot of user-generated data is available these days from huge platforms, blogs, websites, and other review sites. These data are usually unstructured. Analyzing sentiments from these data automatically is considered an important challenge. Several machine learning algorithms are implemented to check the opinions from large data sets. A lot of research has been undergone in understanding machine learning approaches to analyze sentiments. Machine learning mainly depends on the data required for model building, and hence, suitable feature exactions techniques also need to be carried. In this chapter, several deep learning approaches, its challenges, and future issues will be addressed. Deep learning techniques are considered important in predicting the sentiments of users. This chapter aims to analyze the deep-learning techniques for predicting sentiments and understanding the importance of several approaches for mining opinions and determining sentiment polarity.

For Better Healthcare Mining Health Data

2017 ◽  
pp. 135-158
Author(s):  
Güney Gürsel
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Healthcare Management ◽  
Support Vector ◽  
Privacy And Security ◽  
Healthcare Data ◽  
Management Fraud ◽  
Use Of Data ◽  
Customer Relation Management ◽  
Vector Machines

Data mining has great contributions to the healthcare such as support for effective treatment, healthcare management, customer relation management, fraud and abuse detection and decision making. The common data mining methods used in healthcare are Artificial Neural Network, Decision trees, Genetic Algorithms, Nearest neighbor method, Logistic regression, Fuzzy logic, Fuzzy based Neural Networks, Bayesian Networks and Support Vector Machines. The most used task is classification. Because of the complexity and toughness of medical domain, data mining is not an easy task to accomplish. In addition, privacy and security of patient data is a big issue to deal with because of the sensitivity of healthcare data. There exist additional serious challenges. This chapter is a descriptive study aimed to provide an acquaintance to data mining and its usage and applications in healthcare domain. The use of Data mining in healthcare informatics and challenges will be examined.

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