scholarly journals Contextual Anonymization for Secondary Use of Big Data in Biomedical Research: Proposal for an Anonymization Matrix

2018 ◽  
Vol 6 (4) ◽  
pp. e47 ◽  
Author(s):  
John Rumbold ◽  
Barbara Pierscionek
2016 ◽  
Author(s):  
John Rumbold ◽  
Barbara Pierscionek

BACKGROUND The current law on anonymization sets the same standard across all situations, which poses a problem for biomedical research. OBJECTIVE We propose a matrix for setting different standards, which is responsive to context and public expectations. METHODS The law and ethics applicable to anonymization were reviewed in a scoping study. Social science on public attitudes and research on technical methods of anonymization were applied to formulate a matrix. RESULTS The matrix adjusts anonymization standards according to the sensitivity of the data and the safety of the place, people, and projects involved. CONCLUSIONS The matrix offers a tool with context-specific standards for anonymization in data research.


Author(s):  
Shaveta Bhatia

 The epoch of the big data presents many opportunities for the development in the range of data science, biomedical research cyber security, and cloud computing. Nowadays the big data gained popularity.  It also invites many provocations and upshot in the security and privacy of the big data. There are various type of threats, attacks such as leakage of data, the third party tries to access, viruses and vulnerability that stand against the security of the big data. This paper will discuss about the security threats and their approximate method in the field of biomedical research, cyber security and cloud computing.


2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
◽  

Abstract Countries have a wide range of lifestyles, environmental exposures and different health(care) systems providing a large natural experiment to be investigated. Through pan-European comparative studies, underlying determinants of population health can be explored and provide rich new insights into the dynamics of population health and care such as the safety, quality, effectiveness and costs of interventions. Additionally, in the big data era, secondary use of data has become one of the major cornerstones of digital transformation for health systems improvement. Several countries are reviewing governance models and regulatory framework for data reuse. Precision medicine and public health intelligence share the same population-based approach, as such, aligning secondary use of data initiatives will increase cost-efficiency of the data conversion value chain by ensuring that different stakeholders needs are accounted for since the beginning. At EU level, the European Commission has been raising awareness of the need to create adequate data ecosystems for innovative use of big data for health, specially ensuring responsible development and deployment of data science and artificial intelligence technologies in the medical and public health sectors. To this end, the Joint Action on Health Information (InfAct) is setting up the Distributed Infrastructure on Population Health (DIPoH). DIPoH provides a framework for international and multi-sectoral collaborations in health information. More specifically, DIPoH facilitates the sharing of research methods, data and results through participation of countries and already existing research networks. DIPoH's efforts include harmonization and interoperability, strengthening of the research capacity in MSs and providing European and worldwide perspectives to national data. In order to be embedded in the health information landscape, DIPoH aims to interact with existing (inter)national initiatives to identify common interfaces, to avoid duplication of the work and establish a sustainable long-term health information research infrastructure. In this workshop, InfAct lays down DIPoH's core elements in coherence with national and European initiatives and actors i.e. To-Reach, eHAction, the French Health Data Hub and ECHO. Pitch presentations on DIPoH and its national nodes will set the scene. In the format of a round table, possible collaborations with existing initiatives at (inter)national level will be debated with the audience. Synergies will be sought, reflections on community needs will be made and expectations on services will be discussed. The workshop will increase the knowledge of delegates around the latest health information infrastructure and initiatives that strive for better public health and health systems in countries. The workshop also serves as a capacity building activity to promote cooperation between initiatives and actors in the field. Key messages DIPoH an infrastructure aiming to interact with existing (inter)national initiatives to identify common interfaces, avoid duplication and enable a long-term health information research infrastructure. National nodes can improve coordination, communication and cooperation between health information stakeholders in a country, potentially reducing overlap and duplication of research and field-work.


2018 ◽  
Vol 123 (12) ◽  
pp. 1282-1284 ◽  
Author(s):  
Fatima Rodriguez ◽  
David Scheinker ◽  
Robert A. Harrington

2017 ◽  
Vol 26 (01) ◽  
pp. 28-37
Author(s):  
F. J. Martin-Sanchez ◽  
V. Aguiar-Pulido ◽  
G. H. Lopez-Campos ◽  
N. Peek ◽  
L. Sacchi

Summary Objectives: To identify common methodological challenges and review relevant initiatives related to the re-use of patient data collected in routine clinical care, as well as to analyze the economic benefits derived from the secondary use of this data. Through the use of several examples, this article aims to provide a glimpse into the different areas of application, namely clinical research, genomic research, study of environmental factors, and population and health services research. This paper describes some of the informatics methods and Big Data resources developed in this context, such as electronic phenotyping, clinical research networks, biorepositories, screening data banks, and wide association studies. Lastly, some of the potential limitations of these approaches are discussed, focusing on confounding factors and data quality. Methods: A series of literature searches in main bibliographic databases have been conducted in order to assess the extent to which existing patient data has been repurposed for research. This contribution from the IMIA working group on “Data mining and Big Data analytics” focuses on the literature published during the last two years, covering the timeframe since the working group’s last survey. Results and Conclusions: Although most of the examples of secondary use of patient data lie in the arena of clinical and health services research, we have started to witness other important applications, particularly in the area of genomic research and the study of health effects of environmental factors. Further research is needed to characterize the economic impact of secondary use across the broad spectrum of translational research.


2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Emilie Baro ◽  
Samuel Degoul ◽  
Régis Beuscart ◽  
Emmanuel Chazard

Objective.The aim of this study was to provide a definition of big data in healthcare.Methods.A systematic search of PubMed literature published until May 9, 2014, was conducted. We noted the number of statistical individuals(n)and the number of variables(p)for all papers describing a dataset. These papers were classified into fields of study. Characteristics attributed to big data by authors were also considered. Based on this analysis, a definition of big data was proposed.Results.A total of 196 papers were included. Big data can be defined as datasets withLog⁡(n*p)≥7. Properties of big data are its great variety and high velocity. Big data raises challenges on veracity, on all aspects of the workflow, on extracting meaningful information, and on sharing information. Big data requires new computational methods that optimize data management. Related concepts are data reuse, false knowledge discovery, and privacy issues.Conclusion.Big data is defined by volume. Big data should not be confused with data reuse: data can be big without being reused for another purpose, for example, in omics. Inversely, data can be reused without being necessarily big, for example, secondary use of Electronic Medical Records (EMR) data.


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