Consumer Inertia and Competition-Sensitive Data Governance: The Case of Open Banking

2020 ◽  
Author(s):  
Oscar Borgogno ◽  
Giuseppe Colangelo
Keyword(s):  
Data Governance ◽  
Sensitive Data

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.

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.

scholarly journals Global Open Data in Agriculture and Nutrition (GODAN) initiative partner network analysis

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 47
Author(s):  
Ruthie Musker ◽  
Ben Schaap
Keyword(s):  
Business Models ◽  
Stakeholder Involvement ◽  
Open Data ◽  
Free Text ◽  
Multiple Choice Questions ◽  
Data Governance ◽  
Sensitive Data ◽  
Sustainable Business ◽  
Wide Range ◽  
Effective Decision Making

Background: Ensuring healthy, safe and nutritious food for everyone is a global concern. Accessing the information to make the correct decisions regarding food security can be challenging. Open data has been shown to help solve practical problems related to agriculture and nutrition, enabling effective decision-making. In order to create a global data ecosystem that benefits everyone, a wide range of stakeholders must be included in the conversations. The GODAN initiative involves a network of over 500 partner organizations committed to open data in agriculture and nutrition. Methods: We analysed data from a survey of the partner organizations, with 225 respondents, to determine open data activities, including challenges, use of open data, stakeholder involvement and future directions. Respondents were asked a variety of free text and multiple choice questions. Results: 160 partners had at least one open data activity, 65 did not, or did not know. Of the 160, 36 had a second activity. Overall, GODAN partners are developing 200 open data activities. Agriculture is the most common focus for an open data activity. Nutrition-only activities are strongly underrepresented. The most frequently mentioned challenge was cost, which is linked to data governance, management, and human capacity; many do not have the funding to begin or maintain open data activities. Conclusions: The most common challenges were the ones related to the data itself, including how to access it, manage it, and how to keep the sensitive data secure. GODAN is already focusing on these issues through the Responsible Data and Data Ownership pieces. Capacity building, and empowering partners with the tools they need to act, is one of the most effective actions available for GODAN. Funding for open data, as well as research to create more sustainable business models, should be the focus of the open data agenda.

scholarly journals Permissioned Blockchains: Towards Privacy Management and Data Regulation Compliance

2020 ◽  
Author(s):  
Paulo Henrique Alves ◽  
Isabella Z. Frajhof ◽  
Fernando A. Correia ◽  
Clarisse De Souza ◽  
Helio Lopes
Keyword(s):  
Data Protection ◽  
Data Privacy ◽  
Distributed Database ◽  
Analytical Framework ◽  
Data Governance ◽  
Sensitive Data ◽  
Significant Control ◽  
Blockchain Technology ◽  
Governance Model ◽  
Permissioned Blockchains

Data privacy and protection has been a trending topic in recent years. The COVID 19 pandemic has brought about additional challenges and tensions. For example, sharing health data across several organizations is crucial for significant control and reduction of massive infection and death risks. This implies the need for broadly collecting and using personal and sensitive data, which raises the complexity of data protection and privacy challenges. Permissioned blockchain technology is one way to empower users in controlling how their data flows through the net, in a transparent and secure way, through an immutable, unified, and distributed database ruled by smart contracts. Given this background, we developed a second layer data governance model for permissioned blockchains based on the Governance Analytical Framework principles to be applied in pandemic situations. The model has been designed to organize the relationship between data subjects, data controller, and data processor. Regarding privacy concerns, our proposal complies with the Brazilian General Data Protection Law.

A Study on the Introduction of Big Data Governance into Cultural Governance

2019 ◽  
Vol 41 (2) ◽  
pp. 75-106
Author(s):  
Sunyoung Kim ◽  
Byungwoong Kwon
Keyword(s):  
Big Data ◽  
Data Governance ◽  
Cultural Governance

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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