A Review of Trusted Research Environments to Support Next Generation Capabilities based on Interview Analysis (Preprint)

2021 ◽  
Author(s):  
Sanaz Kavianpour ◽  
James Sutherland ◽  
Esma Mansouri-Benssassi ◽  
Natalie Coull ◽  
Emily Jefferson
Keyword(s):  
Artificial Intelligence ◽  
Next Generation ◽  
Security Measures ◽  
Data Types ◽  
Sensitive Data ◽  
Privacy And Security ◽  
Principles And Standards ◽  
Access To Data ◽  
The Uk ◽  
Automation Tools

BACKGROUND A Trusted Research Environment (also known as a Safe Haven) is an environment supported by trained staff and agreed processes (principles and standards) providing access to data for research whilst protecting patient confidentially. Accessing sensitive data without compromising the privacy and security of the data is a complex process. OBJECTIVE This paper presents the security measures, administrative procedures and technical approaches adopted by TREs. METHODS We contacted TRE operators, 20 of whom, in the UK and internationally, agreed to be interviewed remotely under a non-disclosure agreement and to complete a questionnaire about their TRE. RESULTS We observed many similar processes and standards which TREs follow to adhere to the Seven Safes principles. The security processes and TRE capabilities for supporting observational studies using classical statistical methods were mature and the requirements well understood. However, we identified limitations in the security measures and capabilities of TREs to support “next-generation” requirements such as wider ranges of data types, the ability to develop artificial intelligence algorithms and software within the environment, the handling of big data, and timely import and export of data. CONCLUSIONS We found a lack of software/automation tools to support the community and limited knowledge of how to meet next-generation requirements from the research community. Disclosure control for exporting artificial intelligence (AI) algorithms and software was found to be particularly challenging where there is a clear need for additional controls to support this capability within TREs.

THE NEXT GENERATION SPATIAL DATA WAREHOUSE

2002 ◽  
Vol 42 (1) ◽  
pp. 633
Author(s):  
A.J. Yardley
Keyword(s):  
Spatial Data ◽  
Spatial Information ◽  
Essential Element ◽  
Spatial Location ◽  
Geographic Information ◽  
Environmental Data ◽  
Next Generation ◽  
Data Types ◽  
Political Boundaries ◽  
Access To Data

Woodside Energy, based in Perth, Western Australia, has commenced the implementation of its next generation spatial data warehousing and visualisation system. The warehouse facilitates access to data in various corporate geoscience data sets, as well as up-to-date cultural and environmental data. It expands the capabilities of the existing geoscience database by providing a facility to handle spatial data at the database level rather than in files and maps. Spatial data can now be kept in the database, in its correct spatial location, and with a known provenance.Woodside’s worldwide exploration, development and production activities require the use of a wide variety of geographic data such as seismic, bathymetry, wells, permits, coastlines, political boundaries, navigation charts, remote sensing and geological interpretations.Geo-spatial data comes to Woodside in a variety of formats, datums and conditions. The Geomatics Department, through the Geoscience Database and Spatial Information Management teams, loads, maintains and manages all data considered to be corporate. It is quality controlled and placed into the warehouse, where it is readily accessible to technical and administrative staff.Location is an essential element in most Woodside decisions. Because of the new spatial capabilities, a number of geographic information processes are now possible. Additionally information can also be made available through the internet if required.Reliable geographic information will become more widely available in the organisation, and be more easily merged with traditional data types, enhancing the decision-making process.

Changing Dynamics of Network Security involving Hacking/Cracking with Next Generation Firewalls (NGFW)

2016 ◽  
pp. 313-328 ◽  
Author(s):  
Alok Vishwakarma ◽  
Wafa Waheeda S
Keyword(s):  
Network Security ◽  
The Internet ◽  
Next Generation ◽  
Security Measures ◽  
Security Attacks ◽  
Sensitive Data ◽  
Application Security ◽  
Day By Day

With increasing number of users on the internet, risk of security and probability of vulnerable attacks are increasing day by day. For every user connected to network, security attacks like hacking and cracking are very frequent which leaves enormous amounts of sensitive data at the risk of being altered, lost or misused. This apparently leads to the need for security measures on ports and protocols also search for application security, VPN, IPS, and a firewall support. The hacking and cracking threats and attacks in a network are no longer in control with the existing methods and standard firewalls. The introduction of Next Generation Firewalls leads to improved security over network. This chapter deals with hacking and cracking attacks over network and their countermeasures, also focusing on the changing dynamics of network security with next generation firewalls.

scholarly journals Towards Better Performance for Protected Iris Biometric System with Confidence Matrix

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 910
Author(s):  
Tong-Yuen Chai ◽  
Bok-Min Goi ◽  
Wun-She Yap
Keyword(s):  
Data Privacy ◽  
Recognition Performance ◽  
Generalized Solution ◽  
Performance Degradation ◽  
Public Confidence ◽  
Data Types ◽  
Privacy And Security ◽  
Biometric Data ◽  
Biometric Template ◽  
Template Protection

Biometric template protection (BTP) schemes are implemented to increase public confidence in biometric systems regarding data privacy and security in recent years. The introduction of BTP has naturally incurred loss of information for security, which leads to performance degradation at the matching stage. Although efforts are shown in the extended work of some iris BTP schemes to improve their recognition performance, there is still a lack of a generalized solution for this problem. In this paper, a trainable approach that requires no further modification on the protected iris biometric templates has been proposed. This approach consists of two strategies to generate a confidence matrix to reduce the performance degradation of iris BTP schemes. The proposed binary confidence matrix showed better performance in noisy iris data, whereas the probability confidence matrix showed better performance in iris databases with better image quality. In addition, our proposed scheme has also taken into consideration the potential effects in recognition performance, which are caused by the database-associated noise masks and the variation in biometric data types produced by different iris BTP schemes. The proposed scheme has reported remarkable improvement in our experiments with various publicly available iris research databases being tested.

scholarly journals Alliance of Genome Resources Portal: unified model organism research platform

2019 ◽  
Vol 48 (D1) ◽  
pp. D650-D658 ◽  
Author(s):  
◽  
Julie Agapite ◽  
Laurent-Philippe Albou ◽  
Suzi Aleksander ◽  
Joanna Argasinska ◽  
...  
Keyword(s):  
Gene Ontology ◽  
Model Organism ◽  
Model Organisms ◽  
Data Types ◽  
Primary Model ◽  
Genomic Studies ◽  
Health And Disease ◽  
Extensive Body ◽  
Access To Data ◽  
Model Organism Databases

Abstract The Alliance of Genome Resources (Alliance) is a consortium of the major model organism databases and the Gene Ontology that is guided by the vision of facilitating exploration of related genes in human and well-studied model organisms by providing a highly integrated and comprehensive platform that enables researchers to leverage the extensive body of genetic and genomic studies in these organisms. Initiated in 2016, the Alliance is building a central portal (www.alliancegenome.org) for access to data for the primary model organisms along with gene ontology data and human data. All data types represented in the Alliance portal (e.g. genomic data and phenotype descriptions) have common data models and workflows for curation. All data are open and freely available via a variety of mechanisms. Long-term plans for the Alliance project include a focus on coverage of additional model organisms including those without dedicated curation communities, and the inclusion of new data types with a particular focus on providing data and tools for the non-model-organism researcher that support enhanced discovery about human health and disease. Here we review current progress and present immediate plans for this new bioinformatics resource.

Leveraging Twitter Using Artificial Intelligence to Explore Mental Health Insights in the UK During the COVID-19 Pandemic (Preprint)

2021 ◽  
Author(s):  
Christopher Marshall ◽  
Kate Lanyi ◽  
Rhiannon Green ◽  
Georgie Wilkins ◽  
Fiona Pearson ◽  
...  
Keyword(s):  
Mental Health ◽  
Artificial Intelligence ◽  
Language Processing ◽  
Mental Health Problems ◽  
Well Being ◽  
Research Activity ◽  
Health Crisis ◽  
Key Topics ◽  
The Uk ◽  
The Impact

BACKGROUND There is increasing need to explore the value of soft-intelligence, leveraged using the latest artificial intelligence (AI) and natural language processing (NLP) techniques, as a source of analysed evidence to support public health research activity and decision-making. OBJECTIVE The aim of this study was to further explore the value of soft-intelligence analysed using AI through a case study, which examined a large collection of UK tweets relating to mental health during the COVID-19 pandemic. METHODS A search strategy comprising a list of terms related to mental health, COVID-19, and lockdown restrictions was developed to prospectively collate relevant tweets via Twitter’s advanced search application programming interface over a 24-week period. We deployed a specialist NLP platform to explore tweet frequency and sentiment across the UK and identify key topics of discussion. A series of keyword filters were used to clean the initial data retrieved and also set up to track specific mental health problems. Qualitative document analysis was carried out to further explore and expand upon the results generated by the NLP platform. All collated tweets were anonymised RESULTS We identified and analysed 286,902 tweets posted from UK user accounts from 23 July 2020 to 6 January 2021. The average sentiment score was 50%, suggesting overall neutral sentiment across all tweets over the study period. Major fluctuations in volume and sentiment appeared to coincide with key changes to any local and/or national social-distancing measures. Tweets around mental health were polarising, discussed with both positive and negative sentiment. Key topics of consistent discussion over the study period included the impact of the pandemic on people’s mental health (both positively and negatively), fear and anxiety over lockdowns, and anger and mistrust toward the government. CONCLUSIONS Through the primary use of an AI-based NLP platform, we were able to rapidly mine and analyse emerging health-related insights from UK tweets into how the pandemic may be impacting people’s mental health and well-being. This type of real-time analysed evidence could act as a useful intelligence source that agencies, local leaders, and health care decision makers can potentially draw from, particularly during a health crisis.

The National Schools’ Observatory: Access to the Universe for All

2019 ◽  
Vol 15 (S367) ◽  
pp. 28-29
Author(s):  
Sally E. Cooper
Keyword(s):  
Free Access ◽  
Next Generation ◽  
Robotic Telescopes ◽  
The Uk ◽  
Robotic Telescope ◽  
The Universe

AbstractThe National Schools’ Observatory is an educational platform that offers free access to all schools in the UK and Ireland to the world’s largest robotic telescope, the Liverpool Telescope. The website offers activities, resources for teaching and importantly Go Observing, the telescope interface. The website receives 1.5 million visitors a year and has registered users in 80 countries. The next generation of robotic telescopes offer a unique opportunity to build in education, that is open and accessible to all.

Optimal Packet Camouflage Against Traffic Analysis

2021 ◽  
Vol 24 (3) ◽  
pp. 1-23
Author(s):  
Louma Chaddad ◽  
Ali Chehab ◽  
Imad H. Elhajj ◽  
Ayman Kayssi
Keyword(s):  
Classification Accuracy ◽  
Traffic Analysis ◽  
Sensitive Data ◽  
Privacy And Security ◽  
Trade Off ◽  
Network Applications ◽  
Extensive Evaluation ◽  
Flow Features ◽  
Data Content ◽  
Packet Length

Research has proved that supposedly secure encrypted network traffic is actually threatened by privacy and security violations from many aspects. This is mainly due to flow features leaking evidence about user activity and data content. Currently, adversaries can use statistical traffic analysis to create classifiers for network applications and infer users’ sensitive data. In this article, we propose a system that optimally prevents traffic feature leaks. In our first algorithm, we model the packet length probability distribution of the source app to be protected and that of the target app that the source app will resemble. We define a model that mutates the packet lengths of a source app to those lengths from the target app having similar bin probability. This would confuse a classifier by identifying a mutated source app as the target app. In our second obfuscation algorithm, we present an optimized scheme resulting in a trade-off between privacy and complexity overhead. For this reason, we propose a mathematical model for network obfuscation. We formulate analytically the problem of selecting the target app and the length from the target app to mutate to. Then, we propose an algorithm to solve it dynamically. Extensive evaluation of the proposed models, on real app traffic traces, shows significant obfuscation efficiency with relatively acceptable overhead. We were able to reduce a classification accuracy from 91.1% to 0.22% using the first algorithm, with 11.86% padding overhead. The same classification accuracy was reduced to 1.76% with only 0.73% overhead using the second algorithm.

High national acceptance for COVID-19 contact tracing technologies in Taiwan (Preprint)

2021 ◽  
Author(s):  
Paul M. Garrett ◽  
Yuwen Wang ◽  
Joshua P. White ◽  
Yoshihisa Kashima ◽  
Simon Dennis ◽  
...  
Keyword(s):  
Technology Acceptance ◽  
Contact Tracing ◽  
Virus Spread ◽  
Security Measures ◽  
Contributing Factors ◽  
Privacy And Security ◽  
Social Distancing ◽  
The Public ◽  
Privacy Measures ◽  
Credible Intervals

BACKGROUND Governments worldwide have introduced COVID-19 tracing technologies. Taiwan, a world leader in controlling the virus’ spread, has introduced the Taiwan ‘Social Distancing App’ to facilitate COVID-19 contact tracing. However, for these technologies to be effective, they must be accepted and used by the public. OBJECTIVE Our study aimed to determine public acceptance for three hypothetical tracing technologies: a centralized Government App, a decentralized Bluetooth App (e.g., Taiwan’s Social Distancing App), and a Telecommunication tracing technology; and model what factors contributed to their acceptance. METHODS Four nationally representative surveys were conducted in April 2020 sampling 6,000 Taiwanese residents. Perceptions and impacts of COVID-19, government effectiveness, worldviews, and attitudes towards and acceptance of one-of-three hypothetical tracing technologies were assessed. RESULTS Technology acceptance was high across all hypothetical technologies (67% - 73%) and improved with additional privacy measures (82% - 88%). Bayesian modelling (using 95% highest density credible intervals) showed data sensitivity and perceived poor COVID-19 policy compliance inhibited technology acceptance. By contrast, technology benefits (e.g., returning to activities, reducing virus spread, lowering the likelihood of infection), higher education, and perceived technology privacy, security, and trust, were all contributing factors to overall acceptance. Bayesian ordinal probit models revealed higher COVID-19 concern for other people than for one’s self. CONCLUSIONS Taiwan is currently using a range of technologies to minimize the spread of COVID-19 as the country returns to normal economic and social activities. We observed high acceptance for COVID-19 tracing technologies among the Taiwanese public, a promising and necessary finding for the successful introduction of Taiwan’s new ‘Social Distancing App’. Policy makers may capitalize on this acceptance by focusing attention towards the App’s benefits, privacy and security measures, making the App’s privacy measures transparent to the public, and emphasizing App uptake and compliance among the public. CLINICALTRIAL Not applicable.

scholarly journals Privacy and Security Issues in Online Social Networks

2018 ◽  
Vol 10 (12) ◽  
pp. 114 ◽  
Author(s):  
Shaukat Ali ◽  
Naveed Islam ◽  
Azhar Rauf ◽  
Ikram Din ◽  
Mohsen Guizani ◽  
...  
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Virtual Communities ◽  
Service Providers ◽  
Security And Privacy ◽  
Security Issue ◽  
Sensitive Data ◽  
Privacy And Security ◽  
Security Issues ◽  
Social Sphere

The advent of online social networks (OSN) has transformed a common passive reader into a content contributor. It has allowed users to share information and exchange opinions, and also express themselves in online virtual communities to interact with other users of similar interests. However, OSN have turned the social sphere of users into the commercial sphere. This should create a privacy and security issue for OSN users. OSN service providers collect the private and sensitive data of their customers that can be misused by data collectors, third parties, or by unauthorized users. In this paper, common security and privacy issues are explained along with recommendations to OSN users to protect themselves from these issues whenever they use social media.

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