Disclosure control and data dissemination
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All (8)
All (8) ((8 results))
- Articles and reports: 11-522-X201700014719Description:
Open Data initiatives are transforming how governments and other public institutions interact and provide services to their constituents. They increase transparency and value to citizens, reduce inefficiencies and barriers to information, enable data-driven applications that improve public service delivery, and provide public data that can stimulate innovative business opportunities. As one of the first international organizations to adopt an open data policy, the World Bank has been providing guidance and technical expertise to developing countries that are considering or designing their own initiatives. This presentation will give an overview of developments in open data at the international level along with current and future experiences, challenges, and opportunities. Mr. Herzog will discuss the rationales under which governments are embracing open data, demonstrated benefits to both the public and private sectors, the range of different approaches that governments are taking, and the availability of tools for policymakers, with special emphasis on the roles and perspectives of National Statistics Offices within a government-wide initiative.
Release date: 2016-03-24 - 2. Statistics Canada and Open Data ArchivedArticles and reports: 11-522-X201700014720Description:
This paper is intended to give a brief overview of Statistics Canada’s involvement with open data. It will first discuss how the principles of open data are being adopted in the agency’s ongoing dissemination practices. It will then discuss the agency’s involvement with the whole of government open data initiative. This involvement is twofold: Statistics Canada is the major data contributor to the Government of Canada Open Data portal, but also plays an important behind the scenes role as the service provider responsible for developing and maintaining the Open Data portal (which is now part of the wider Open Government portal.)
Release date: 2016-03-24 - 3. Exploring Canada’s Open Government Portal ArchivedArticles and reports: 11-522-X201700014721Description:
Open data is becoming an increasingly important expectation of Canadians, researchers, and developers. Learn how and why the Government of Canada has centralized the distribution of all Government of Canada open data through Open.Canada.ca and how this initiative will continue to support the consumption of statistical information.
Release date: 2016-03-24 - Articles and reports: 11-522-X201700014731Description:
Our study describes various factors that are of concern when evaluating disclosure risk of contextualized microdata and some of the empirical steps that are involved in their assessment. Utilizing synthetic sets of survey respondents, we illustrate how different postulates shape the assessment of risk when considering: (1) estimated probabilities that unidentified geographic areas are represented within a survey; (2) the number of people in the population who share the same personal and contextual identifiers as a respondent; and (3) the anticipated amount of coverage error in census population counts and extant files that provide identifying information (like names and addresses).
Release date: 2016-03-24 - 5. A Modern Job Submission Application to Access IAB’s Confidential Administrative and Survey Research Data ArchivedArticles and reports: 11-522-X201700014732Description:
The Institute for Employment Research (IAB) is the research unit of the German Federal Employment Agency. Via the Research Data Centre (FDZ) at the IAB, administrative and survey data on individuals and establishments are provided to researchers. In cooperation with the Institute for the Study of Labor (IZA), the FDZ has implemented the Job Submission Application (JoSuA) environment which enables researchers to submit jobs for remote data execution through a custom-built web interface. Moreover, two types of user-generated output files may be distinguished within the JoSuA environment which allows for faster and more efficient disclosure review services.
Release date: 2016-03-24 - 6. Enhancing data sharing via "safe designs" ArchivedArticles and reports: 11-522-X201700014733Description:
The social value of data collections are dramatically enhanced by the broad dissemination of research files and the resulting increase in scientific productivity. Currently, most studies are designed with a focus on collecting information that is analytically useful and accurate, with little forethought as to how it will be shared. Both literature and practice also presume that disclosure analysis will take place after data collection. But to produce public-use data of the highest analytical utility for the largest user group, disclosure risk must be considered at the beginning of the research process. Drawing upon economic and statistical decision-theoretic frameworks and survey methodology research, this study seeks to enhance the scientific productivity of shared research data by describing how disclosure risk can be addressed in the earliest stages of research with the formulation of "safe designs" and "disclosure simulations", where an applied statistical approach has been taken in: (1) developing and validating models that predict the composition of survey data under different sampling designs; (2) selecting and/or developing measures and methods used in the assessments of disclosure risk, analytical utility, and disclosure survey costs that are best suited for evaluating sampling and database designs; and (3) conducting simulations to gather estimates of risk, utility, and cost for studies with a wide range of sampling and database design characteristics.
Release date: 2016-03-24 - Articles and reports: 11-522-X201700014734Description:
Data protection and privacy are key challenges that need to be tackled with high priority in order to enable the use of Big Data in the production of Official Statistics. This was emphasized in 2013 by the Directors of National Statistical Insitutes (NSIs) of the European Statistical System Committee (ESSC) in the Scheveningen Memorandum. The ESSC requested Eurostat and the NSIs to elaborate an action plan with a roadmap for following up the implementation of the Memorandum. At the Riga meeting on September 26, 2014, the ESSC endorsed the Big Data Action Plan and Roadmap 1.0 (BDAR) presented by the Eurostat Task Force on Big Data (TFBD) and agreed to integrate it into the ESS Vision 2020 portfolio. Eurostat also collaborates in this field with external partners such as the United Nations Economic Commission for Europe (UNECE). The big data project of the UNECE High-Level Group is an international project on the role of big data in the modernization of statistical production. It comprised four ‘task teams’ addressing different aspects of Big Data issues relevant for official statistics: Privacy, Partnerships, Sandbox, and Quality. The Privacy Task Team finished its work in 2014 and gave an overview of the existing tools for risk management regarding privacy issues, described how risk of identification relates to Big Data characteristics and drafted recommendations for National Statistical Offices (NSOs). It mainly concluded that extensions to existing frameworks, including use of new technologies were needed in order to deal with privacy risks related to the use of Big Data. The BDAR builds on the work achieved by the UNECE task teams. Specifically, it recognizes that a number of big data sources contain sensitive information, that their use for official statistics may induce negative perceptions with the general public and other stakeholders and that this risk should be mitigated in the short to medium term. It proposes to launch multiple actions like e.g., an adequate review on ethical principles governing the roles and activities of the NSIs and a strong communication strategy. The paper presents the different actions undertaken within the ESS and in collaboration with UNECE, as well as potential technical and legal solutions to be put in place in order to address the data protection and privacy risks in the use of Big Data for Official Statistics.
Release date: 2016-03-24 - Articles and reports: 11-522-X201700014735Description:
Microdata dissemination normally requires data reduction and modification methods be applied, and the degree to which these methods are applied depend on the control methods that will be required to access and use the data. An approach that is in some circumstances more suitable for accessing data for statistical purposes is secure computation, which involves computing analytic functions on encrypted data without the need to decrypt the underlying source data to run a statistical analysis. This approach also allows multiple sites to contribute data while providing strong privacy guarantees. This way the data can be pooled and contributors can compute analytic functions without either party knowing their inputs. We explain how secure computation can be applied in practical contexts, with some theoretical results and real healthcare examples.
Release date: 2016-03-24
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Analysis (8)
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- Articles and reports: 11-522-X201700014719Description:
Open Data initiatives are transforming how governments and other public institutions interact and provide services to their constituents. They increase transparency and value to citizens, reduce inefficiencies and barriers to information, enable data-driven applications that improve public service delivery, and provide public data that can stimulate innovative business opportunities. As one of the first international organizations to adopt an open data policy, the World Bank has been providing guidance and technical expertise to developing countries that are considering or designing their own initiatives. This presentation will give an overview of developments in open data at the international level along with current and future experiences, challenges, and opportunities. Mr. Herzog will discuss the rationales under which governments are embracing open data, demonstrated benefits to both the public and private sectors, the range of different approaches that governments are taking, and the availability of tools for policymakers, with special emphasis on the roles and perspectives of National Statistics Offices within a government-wide initiative.
Release date: 2016-03-24 - 2. Statistics Canada and Open Data ArchivedArticles and reports: 11-522-X201700014720Description:
This paper is intended to give a brief overview of Statistics Canada’s involvement with open data. It will first discuss how the principles of open data are being adopted in the agency’s ongoing dissemination practices. It will then discuss the agency’s involvement with the whole of government open data initiative. This involvement is twofold: Statistics Canada is the major data contributor to the Government of Canada Open Data portal, but also plays an important behind the scenes role as the service provider responsible for developing and maintaining the Open Data portal (which is now part of the wider Open Government portal.)
Release date: 2016-03-24 - 3. Exploring Canada’s Open Government Portal ArchivedArticles and reports: 11-522-X201700014721Description:
Open data is becoming an increasingly important expectation of Canadians, researchers, and developers. Learn how and why the Government of Canada has centralized the distribution of all Government of Canada open data through Open.Canada.ca and how this initiative will continue to support the consumption of statistical information.
Release date: 2016-03-24 - Articles and reports: 11-522-X201700014731Description:
Our study describes various factors that are of concern when evaluating disclosure risk of contextualized microdata and some of the empirical steps that are involved in their assessment. Utilizing synthetic sets of survey respondents, we illustrate how different postulates shape the assessment of risk when considering: (1) estimated probabilities that unidentified geographic areas are represented within a survey; (2) the number of people in the population who share the same personal and contextual identifiers as a respondent; and (3) the anticipated amount of coverage error in census population counts and extant files that provide identifying information (like names and addresses).
Release date: 2016-03-24 - 5. A Modern Job Submission Application to Access IAB’s Confidential Administrative and Survey Research Data ArchivedArticles and reports: 11-522-X201700014732Description:
The Institute for Employment Research (IAB) is the research unit of the German Federal Employment Agency. Via the Research Data Centre (FDZ) at the IAB, administrative and survey data on individuals and establishments are provided to researchers. In cooperation with the Institute for the Study of Labor (IZA), the FDZ has implemented the Job Submission Application (JoSuA) environment which enables researchers to submit jobs for remote data execution through a custom-built web interface. Moreover, two types of user-generated output files may be distinguished within the JoSuA environment which allows for faster and more efficient disclosure review services.
Release date: 2016-03-24 - 6. Enhancing data sharing via "safe designs" ArchivedArticles and reports: 11-522-X201700014733Description:
The social value of data collections are dramatically enhanced by the broad dissemination of research files and the resulting increase in scientific productivity. Currently, most studies are designed with a focus on collecting information that is analytically useful and accurate, with little forethought as to how it will be shared. Both literature and practice also presume that disclosure analysis will take place after data collection. But to produce public-use data of the highest analytical utility for the largest user group, disclosure risk must be considered at the beginning of the research process. Drawing upon economic and statistical decision-theoretic frameworks and survey methodology research, this study seeks to enhance the scientific productivity of shared research data by describing how disclosure risk can be addressed in the earliest stages of research with the formulation of "safe designs" and "disclosure simulations", where an applied statistical approach has been taken in: (1) developing and validating models that predict the composition of survey data under different sampling designs; (2) selecting and/or developing measures and methods used in the assessments of disclosure risk, analytical utility, and disclosure survey costs that are best suited for evaluating sampling and database designs; and (3) conducting simulations to gather estimates of risk, utility, and cost for studies with a wide range of sampling and database design characteristics.
Release date: 2016-03-24 - Articles and reports: 11-522-X201700014734Description:
Data protection and privacy are key challenges that need to be tackled with high priority in order to enable the use of Big Data in the production of Official Statistics. This was emphasized in 2013 by the Directors of National Statistical Insitutes (NSIs) of the European Statistical System Committee (ESSC) in the Scheveningen Memorandum. The ESSC requested Eurostat and the NSIs to elaborate an action plan with a roadmap for following up the implementation of the Memorandum. At the Riga meeting on September 26, 2014, the ESSC endorsed the Big Data Action Plan and Roadmap 1.0 (BDAR) presented by the Eurostat Task Force on Big Data (TFBD) and agreed to integrate it into the ESS Vision 2020 portfolio. Eurostat also collaborates in this field with external partners such as the United Nations Economic Commission for Europe (UNECE). The big data project of the UNECE High-Level Group is an international project on the role of big data in the modernization of statistical production. It comprised four ‘task teams’ addressing different aspects of Big Data issues relevant for official statistics: Privacy, Partnerships, Sandbox, and Quality. The Privacy Task Team finished its work in 2014 and gave an overview of the existing tools for risk management regarding privacy issues, described how risk of identification relates to Big Data characteristics and drafted recommendations for National Statistical Offices (NSOs). It mainly concluded that extensions to existing frameworks, including use of new technologies were needed in order to deal with privacy risks related to the use of Big Data. The BDAR builds on the work achieved by the UNECE task teams. Specifically, it recognizes that a number of big data sources contain sensitive information, that their use for official statistics may induce negative perceptions with the general public and other stakeholders and that this risk should be mitigated in the short to medium term. It proposes to launch multiple actions like e.g., an adequate review on ethical principles governing the roles and activities of the NSIs and a strong communication strategy. The paper presents the different actions undertaken within the ESS and in collaboration with UNECE, as well as potential technical and legal solutions to be put in place in order to address the data protection and privacy risks in the use of Big Data for Official Statistics.
Release date: 2016-03-24 - Articles and reports: 11-522-X201700014735Description:
Microdata dissemination normally requires data reduction and modification methods be applied, and the degree to which these methods are applied depend on the control methods that will be required to access and use the data. An approach that is in some circumstances more suitable for accessing data for statistical purposes is secure computation, which involves computing analytic functions on encrypted data without the need to decrypt the underlying source data to run a statistical analysis. This approach also allows multiple sites to contribute data while providing strong privacy guarantees. This way the data can be pooled and contributors can compute analytic functions without either party knowing their inputs. We explain how secure computation can be applied in practical contexts, with some theoretical results and real healthcare examples.
Release date: 2016-03-24
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