scholarly journals A Novel Data Governance Scheme Based on the Behavioral Economics Theory

2021 ◽  
Author(s):  
Bohan Hou
Keyword(s):  
Behavioral Economics ◽  
Data Transfer ◽  
Data Governance ◽  
Sensitive Data ◽  
Design Elements ◽  
Management Platform ◽  
Asset Class ◽  
Data Management Platform ◽  
Data Request ◽  
Global Data

The digital economy has become one of the most important sectors in global GDP.Personal data is the new asset class that creates value through the applications ofcybertechnologies and Artificial Intelligence. However, there are increasing concerns over the privacy invasions and human rights violations associated with the exploitation ofpersonal data.Various data laws were made in nations to balance the data fluidity and privacy protections. However, most laws have inherent limitations and underenforcement issuesthat fail to achieve their aims and protection principles. Utilizing a behavioral economics theoretical framework, this study categorizes the issues and causes to InformationAsymmetry, Bounded Rationality, Power Imbalance, and Technical Incapacity.The study makes a novel contribution by proposing a global data governance scheme to address the limitations of data laws. The scheme adopts a Libertarian Paternalism approach and develops seven principles in the framework design. Elements and components in the scheme include individuals, data controllers, privacy rating frameworks, meta-data and privacy configuration, reports, Automated Consent Management (ACM), Bureaus, and signatures, etc. The components will operate on an interoperable and global data management platform. Visual diagrams are developed to describe the various forms of interactions between components and procedures.A balance between privacy protection and data fluidity is found through experimental scenarios such as Ordinary Data Request, Sensitive Data Request, Inconsistency Checks, Data Rights Exercise, Monitored Data Transfer, Broadcast and Notice. The scenarios analyzed are not exhaustive but serve as the meaningful startingpoint to inspire more designs and discussions from scholars.

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.

scholarly journals The Florida Pancreas Collaborative Next-Generation Biobank: Infrastructure to Reduce Disparities and Improve Survival for a Diverse Cohort of Patients with Pancreatic Cancer

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 809
Author(s):  
Jennifer Permuth ◽  
Kaleena Dezsi ◽  
Shraddha Vyas ◽  
Karla Ali ◽  
Toni Basinski ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Pancreatic Tumor ◽  
Minority Groups ◽  
Multidisciplinary Teams ◽  
Critical Gap ◽  
Management Platform ◽  
Medical Institutions ◽  
Computed Tomography Scans ◽  
Donated Blood ◽  
Data Management Platform

Background: Well-annotated, high-quality biorepositories provide a valuable platform to support translational research. However, most biorepositories have poor representation of minority groups, limiting the ability to address health disparities. Methods: We describe the establishment of the Florida Pancreas Collaborative (FPC), the first state-wide prospective cohort study and biorepository designed to address the higher burden of pancreatic cancer (PaCa) in African Americans (AA) compared to Non-Hispanic Whites (NHW) and Hispanic/Latinx (H/L). We provide an overview of stakeholders; study eligibility and design; recruitment strategies; standard operating procedures to collect, process, store, and transfer biospecimens, medical images, and data; our cloud-based data management platform; and progress regarding recruitment and biobanking. Results: The FPC consists of multidisciplinary teams from fifteen Florida medical institutions. From March 2019 through August 2020, 350 patients were assessed for eligibility, 323 met inclusion/exclusion criteria, and 305 (94%) enrolled, including 228 NHW, 30 AA, and 47 H/L, with 94%, 100%, and 94% participation rates, respectively. A high percentage of participants have donated blood (87%), pancreatic tumor tissue (41%), computed tomography scans (76%), and questionnaires (62%). Conclusions: This biorepository addresses a critical gap in PaCa research and has potential to advance translational studies intended to minimize disparities and reduce PaCa-related morbidity and mortality.

scholarly journals Developing Best Practice in Linking and Sharing Administrative Data – A Comparison of Views in Health and Higher Education

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Jackie Street ◽  
Annette Braunack-Mayer ◽  
Stacy Carter ◽  
Tam Ha ◽  
Xiaoqi Feng ◽  
...  
Keyword(s):  
Higher Education ◽  
Administrative Data ◽  
Policy Development ◽  
Best Practice ◽  
Ethical Issues ◽  
Health Sector ◽  
Clear Understanding ◽  
Data Governance ◽  
Sensitive Data ◽  
Wide Range

IntroductionLarge administrative datasets are now being used for secondary purposes across a wide range of public sector organisations, including in health and higher education. However, governance, regulation and policy surrounding the use of these datasets are at different stages of development in these sectors. Our aim was to explore similarities and differences in the use of administrative data between the health and higher education sectors to inform policy development. Objectives and ApproachWe investigated views on the use of administrative data in both the health and higher education sectors. We conducted 18 qualitative in-depth interviews with key stakeholders, to provide insight into the ethical, social and legal issues associated with the use of big data in these settings. The interviews were transcribed and thematically coded. ResultsParticipants indicated the rapid pace of technological change and large volume of potentially sensitive data collected raises governance, infrastructure and ethical issues in both settings. Common challenges include communication, staff capabilities, delays in access, multiple policies and governance committees, and technical and operational issues. In the health sector, there was clear understanding of the issues and governance structures to address these issues, whereas this understanding was more variable in the higher education sector. Trust in government (to use responsibly and store securely) was raised in the health sector but not in universities. Conclusion / ImplicationsUnderstanding and use of administrative data are at quite different levels of development in the higher education and health sectors. Higher education needs policy and ethical guidance and higher level governance and greater consultation across the sector. Both sectors would benefit from a national approach to data governance.

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