Integrating New Zealand census mortality study and New Zealand longitudinal census: privacy impact assessment

This paper outlines the processes for managing the privacy risks raised by integrating two particular census datasets. Overview Integrating New Zealand Census Mortality Study and New Zealand Longitudinal Census: Privacy impact assessment provides a systematic evaluation of the privacy risks associated with integrating datasets from the New Zealand Census Mortality Study (NZCMS) and the New Zealand Longitudinal Census (NZLC). It outlines the processes for managing these risks. The NZCMS comprise datasets of mortality records linked to the 1981–2006 Censuses and the NZLC datasets comprise linked census pairs covering the same period

Understanding Society: design overview

Understanding Society, the UK Household Longitudinal Study, builds on the success of the British Household Panel Survey (BHPS). This paper describes some of the key elements of the design and conduct of the study and suggests how Understanding Society is distinctive as a longitudinal survey. Its large sample size offers new opportunities to study sub-groups that may be too small for separate analysis on other studies. The new content included in Understanding Society, not least the bio-measures, provides exciting prospects for interdisciplinary research across the social and medical sciences. The Innovation Panel is already proving to be an invaluable resource for research in longitudinal survey methodology. Finally, the inclusion of the BHPS sample within Understanding Society enables this long running panel to continue into the future, opening up inter-generational research and the opportunity to look at very long-term trajectories of change. This paper also describes the four sample components: the general population sample, ethnic minority boost sample, the Innovation Panel, and participants from the BHPS. Each component has a multistage sample designs, mostly with stratification and clustering. A complex weighting strategy is being developed to support varied analyses. This overview also describes the instruments, methods of data collection, and the timetable for data collection. A summary of the survey content?s is provided. With the data becoming available the user community is beginning to benefit from this investment in longitudinal studies

Development of an ongoing national data collection on the educational outcomes of children in child protection services: a working paper

This working paper provides an overview of a proposed national linked dataset on the educational activity and outcomes of children while in child protection services, to allow ongoing and longitudinal monitoring of the academic progress, and to better inform policy, practice and planning of activities to support these children.Summary Background Improving the educational outcomes of children involved in statutory child protection services has been a high priority for Australian governments in recent years. The inclusion of education-specific national indicators in the National Framework for Protecting Australia\u27s Children 2009-2020 and the National Standards for out-of-home care means the implementation of an ongoing national data collection on the educational outcomes of children in the care of the state has increased in importance and urgency. Such a collection would allow ongoing and longitudinal monitoring of academic progress, to better inform policy, practice and planning of activities to support these children. This working paper sets out a proposed national methodology for reporting on the educational outcomes of children in child protection services. The former CDSMAC (now SCCDSAC) funded the AIHW to develop this methodology in collaboration with jurisdictions.Proposed methodology National reporting on the educational outcomes of children in care can be best achieved through linking the Child Protection National Minimum Data Set (CP NMDS) with a national set of education data (an \u27Education Module\u27, see Section 2). The CP NMDS is the \u27base\u27 data set for the Education Module and will be used to identify in-scope children. In line with the National Standards for out-of-home care, the population scope of the Education Module would be children aged 0-17 years whose care arrangements have been ordered through the Children\u27s Court, where parental responsibility for the child or young person has been transferred to the Minister/Chief Executive. A range of relevant administrative data sets which capture information across the primary and secondary schooling years have been identified, from which data could be sourced for the Education Module (see Section 2 for details). Undertaking data linkage at the national level will allow the use of nationally-consistent linkage processes to improve match rates and efficiency. The AIHW is a Commonwealth- accredited Data Integration Authority, and therefore well-positioned to undertake this linkage work for the Education Module. A phased approach to implementation is recommended, commencing with linkage between NAPLAN data and CP NMDS data (Phase 1, further described in Section 3). The Education Module could then be expanded following the successful completion of Phase 1.Phase 1 implementationHigh-level support from both the child protection and education sectors will be required to implement the Education Module, which would involve national-level data linkage. In- principle support for the implementation of Phase 1 (further described in Section 3) was received from the appropriate national community services and education committees in early 2013-SCCDSAC, and the Australian Education, Early Childhood Development and Youth Affairs Senior Officials Committee (AEEYSOC). The AIHW has received funding from SCCDSAC to roll out Phase 1 over a period of 18 months, commencing in September 2013

Cancer Informatics for Cancer Centers (CI4CC): Building a Community Focused on Sharing Ideas and Best Practices to Improve Cancer Care and Patient Outcomes.

Cancer Informatics for Cancer Centers (CI4CC) is a grassroots, nonprofit 501c3 organization intended to provide a focused national forum for engagement of senior cancer informatics leaders, primarily aimed at academic cancer centers anywhere in the world but with a special emphasis on the 70 National Cancer Institute-funded cancer centers. Although each of the participating cancer centers is structured differently, and leaders' titles vary, we know firsthand there are similarities in both the issues we face and the solutions we achieve. As a consortium, we have initiated a dedicated listserv, an open-initiatives program, and targeted biannual face-to-face meetings. These meetings are a place to review our priorities and initiatives, providing a forum for discussion of the strategic and pragmatic issues we, as informatics leaders, individually face at our respective institutions and cancer centers. Here we provide a brief history of the CI4CC organization and meeting highlights from the latest CI4CC meeting that took place in Napa, California from October 14-16, 2019. The focus of this meeting was "intersections between informatics, data science, and population science." We conclude with a discussion on "hot topics" on the horizon for cancer informatics

Privacy and Confidentiality in an e-Commerce World: Data Mining, Data Warehousing, Matching and Disclosure Limitation

The growing expanse of e-commerce and the widespread availability of online databases raise many fears regarding loss of privacy and many statistical challenges. Even with encryption and other nominal forms of protection for individual databases, we still need to protect against the violation of privacy through linkages across multiple databases. These issues parallel those that have arisen and received some attention in the context of homeland security. Following the events of September 11, 2001, there has been heightened attention in the United States and elsewhere to the use of multiple government and private databases for the identification of possible perpetrators of future attacks, as well as an unprecedented expansion of federal government data mining activities, many involving databases containing personal information. We present an overview of some proposals that have surfaced for the search of multiple databases which supposedly do not compromise possible pledges of confidentiality to the individuals whose data are included. We also explore their link to the related literature on privacy-preserving data mining. In particular, we focus on the matching problem across databases and the concept of ``selective revelation'' and their confidentiality implications.Comment: Published at http://dx.doi.org/10.1214/088342306000000240 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Patient privacy protection using anonymous access control techniques

Objective: The objective of this study is to develop a solution to preserve security and privacy in a healthcare environment where health-sensitive information will be accessed by many parties and stored in various distributed databases. The solution should maintain anonymous medical records and it should be able to link anonymous medical information in distributed databases into a single patient medical record with the patient identity. Methods: In this paper we present a protocol that can be used to authenticate and authorize patients to healthcare services without providing the patient identification. Healthcare service can identify the patient using separate temporary identities in each identification session and medical records are linked to these temporary identities. Temporary identities can be used to enable record linkage and reverse track real patient identity in critical medical situations. Results: The proposed protocol provides main security and privacy services such as user anonymity, message privacy, message confidentiality, user authentication, user authorization and message replay attacks. The medical environment validates the patient at the healthcare service as a real and registered patient for the medical services. Using the proposed protocol, the patient anonymous medical records at different healthcare services can be linked into one single report and it is possible to securely reverse track anonymous patient into the real identity. Conclusion: The protocol protects the patient privacy with a secure anonymous authentication to healthcare services and medical record registries according to the European and the UK legislations, where the patient real identity is not disclosed with the distributed patient medical records

Business Integration as a Service

This paper presents Business Integration as a Service (BIaS) which enables connections between services operating in the Cloud. BIaS integrates different services and business activities to achieve a streamline process. We illustrate this integration using two services; Return on Investment (ROI) Measurement as a Service (RMaaS) and Risk Analysis as a Service (RAaaS) in two case studies at the University of Southampton and Vodafone/Apple. The University of Southampton case study demonstrates the cost-savings and the risk analysis achieved, so two services can work as a single service. The Vodafone/Apple case study illustrates statistical analysis and 3D Visualisation of expected revenue and associated risk. These two cases confirm the benefits of BIaS adoption, including cost reduction and improvements in efficiency and risk analysis. Implementation of BIaS in other organisations is also discussed. Important data arising from the integration of RMaaS and RAaaS are useful for management of University of Southampton and potential and current investors for Vodafone/Apple

Balancing Access to Data And Privacy. A review of the issues and approaches for the future

Access to sensitive micro data should be provided using remote access data enclaves. These enclaves should be built to facilitate the productive, high-quality usage of microdata. In other words, they should support a collaborative environment that facilitates the development and exchange of knowledge about data among data producers and consumers. The experience of the physical and life sciences has shown that it is possible to develop a research community and a knowledge infrastructure around both research questions and the different types of data necessary to answer policy questions. In sum, establishing a virtual organization approach would provided the research community with the ability to move away from individual, or artisan, science, towards the more generally accepted community based approach. Enclave should include a number of features: metadata documentation capacity so that knowledge about data can be shared; capacity to add data so that the data infrastructure can be augmented; communication capacity, such as wikis, blogs and discussion groups so that knowledge about the data can be deepened and incentives for information sharing so that a community of practice can be built. The opportunity to transform micro-data based research through such a organizational infrastructure could potentially be as far-reaching as the changes that have taken place in the biological and astronomical sciences. It is, however, an open research question how such an organization should be established: whether the approach should be centralized or decentralized. Similarly, it is an open research question as to the appropriate metrics of success, and the best incentives to put in place to achieve success.Methodology for Collecting, Estimating, Organizing Microeconomic Data