scholarly journals TSD: A Research Platform for Sensitive Data

2021 ◽  
Vol 181 ◽  
pp. 127-134
Author(s):  
Egil Øvrelid ◽  
Bendik Bygstad ◽  
Gard Thomassen
Keyword(s):  
Sensitive Data ◽  
Research Platform

scholarly journals GRAPHITE: Geographic Information UK Secure E-Research Platform

2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Richard Fry ◽  
Amy Mizen ◽  
Ashley Akbari ◽  
Simon Thompson
Keyword(s):  
Spatial Data ◽  
Fast Food ◽  
Best Practice ◽  
Information Science ◽  
Geographic Information ◽  
Sensitive Data ◽  
Data Resource ◽  
Research Platform ◽  
The Uk ◽  
Spatiotemporal Models

BackgroundThe use of Geographic Information Science in administrative and health data research provides researchers and policymakers with powerful modelling capabilities when exploring spatial variations in health and social outcomes. However, there are challenges around using spatial data and associated modelling techniques in terms of computing power, skills, data quality and disclosure controls. Main AimIn this case study, we describe our approach to overcoming some of these problems in conjunction with the Secure Anonymised Information Linkage Databank (SAIL). ApproachThe UK Secure E-Research Platform (UKSeRP) is a suite of technologies which can be linked together to provide a secure system to support a particular use case or data resource. We have taken this foundation and built a Geographic Information UKSeRP which is solely focused on the storage, manipulation and generation of spatial data and models to support administrative and health research. The GIS system is built around the Unique Property Reference Number (UPRN) which can be anonymised into a Residential Anonymised Linking Field (RALF) and provides a precise location of households across the UK. This system allows the user to generate high-resolution spatiotemporal models of geographically varying phenomena (e.g. access to services, fast food, alcohol, greenspace and pollution) in a secure managed environment. To support the geographic data stored in the platform there is a range of GIS tools (Python, R, QGIS, PostGIS, OSRM) and standardised methods available to users which sit alongside a data catalogue. Access to the platform is managed via an information governance review process on a project by project basis to ensure best practice in disclosure control and integrity. ConclusionSpatial data and GIS techniques are an important resource for academic and policy researchers. The GRAPHITE platform demonstrates best practice for using spatial data when used in conjunction with sensitive data.

Discovery at sea; a heady mix of scientists, ships and sailors

2005 ◽  
Vol 32 (2) ◽  
pp. 177-191 ◽  
Author(s):  
A. L. Rice
Keyword(s):  
Human Nature ◽  
Research Platform ◽  
Almost All

The success or failure of science at sea depends on many factors, including the suitability of the vessel as a research platform and the support of the scientist's shipmates. This paper touches on a few of the classic examples of unsuitable ships provided for scientists and explorers, such as Wyville Thomson and Carpenter in the Lightning in 1868 and the relatively minor shortcomings of Scott's Discovery. But it concentrates on some of the personality clashes on research or exploration voyages ranging from Edmund Halley's experience of an insubordinate mate in the Paramore in 1698, to Nicolas Baudin's appalling relations with almost all of his companions in the Géographe and Naturaliste in 1800–1803. Since human nature does not change it is suggested that seagoing scientists should pay at least as much attention to the personalities of their prospective shipmates as to the characteristics of the ships in which they expect to sail.

Security Test by using F T M and Data Allocation Strategies on Leakage Detection

2005 ◽  
Vol 4 (2) ◽  
pp. 393-400
Author(s):  
Pallavali Radha ◽  
G. Sireesha
Keyword(s):  
Third Party ◽  
Distributed Data ◽  
Data Allocation ◽  
Security Testing ◽  
Sensitive Data ◽  
Sample Data ◽  
The One ◽  
Security Test ◽  
Data Request ◽  
Allocation Strategies

The data distributors work is to give sensitive data to a set of presumably trusted third party agents.The data i.e., sent to these third parties are available on the unauthorized places like web and or some ones systems, due to data leakage. The distributor must know the way the data was leaked from one or more agents instead of as opposed to having been independently gathered by other means. Our new proposal on data allocation strategies will improve the probability of identifying leakages along with Security attacks typically result from unintended behaviors or invalid inputs.  Due to too many invalid inputs in the real world programs is labor intensive about security testing.The most desirable thing is to automate or partially automate security-testing process. In this paper we represented Predicate/ Transition nets approach for security tests automated generationby using formal threat models to detect the agents using allocation strategies without modifying the original data.The guilty agent is the one who leaks the distributed data. To detect guilty agents more effectively the idea is to distribute the data intelligently to agents based on sample data request and explicit data request. The fake object implementation algorithms will improve the distributor chance of detecting guilty agents.

A Survey on Efand64257cient and Secure Techniques for Storing Sensitive Data on Cloud

2019 ◽  
Vol 7 (5) ◽  
pp. 1766-1777
Author(s):  
Supriya J. ◽  
Srusti K.S. ◽  
amana G ◽  
S. Sukhaniya Ragani ◽  
Raghavendra S. ◽  
...  
Keyword(s):  
Sensitive Data

Mobile Software Assurance Informed through Knowledge Graph Construction: The OWASP Threat of Insecure Data Storage

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Suzanna Schmeelk ◽  
Lixin Tao
Keyword(s):  
Data Storage ◽  
Program Analysis ◽  
Web Application ◽  
Security Analysis ◽  
Knowledge Graph ◽  
Healthcare Applications ◽  
Sensitive Data ◽  
Knowledge Graphs ◽  
Mobile Malware Detection ◽  
Software Assurance

Many organizations, to save costs, are movinheg to t Bring Your Own Mobile Device (BYOD) model and adopting applications built by third-parties at an unprecedented rate.  Our research examines software assurance methodologies specifically focusing on security analysis coverage of the program analysis for mobile malware detection, mitigation, and prevention.  This research focuses on secure software development of Android applications by developing knowledge graphs for threats reported by the Open Web Application Security Project (OWASP).  OWASP maintains lists of the top ten security threats to web and mobile applications.  We develop knowledge graphs based on the two most recent top ten threat years and show how the knowledge graph relationships can be discovered in mobile application source code.  We analyze 200+ healthcare applications from GitHub to gain an understanding of their software assurance of their developed software for one of the OWASP top ten moble threats, the threat of “Insecure Data Storage.”  We find that many of the applications are storing personally identifying information (PII) in potentially vulnerable places leaving users exposed to higher risks for the loss of their sensitive data.

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