Big Data Warehouse for Healthcare-Sensitive Data Applications

Abstract

Obesity is a major public health problem worldwide, and the prevalence of childhood obesity is of particular concern. Effective interventions for preventing and treating childhood obesity aim to change behaviour and exposure at the individual, community, and societal levels. However, monitoring and evaluating such changes is very challenging. The EU Horizon 2020 project “Big Data against Childhood Obesity (BigO)” aims at gathering large-scale data from a large number of children using different sensor technologies to create comprehensive obesity prevalence models for data-driven predictions about specific policies on a community. It further provides real-time monitoring of the population responses, supported by meaningful real-time data analysis and visualisations. Since BigO involves monitoring and storing of personal data related to the behaviours of a potentially vulnerable population, the data representation, security, and access control are crucial. In this paper, we briefly present the BigO system architecture and focus on the necessary components of the system that deals with data access control, storage, anonymisation, and the corresponding interfaces with the rest of the system. We propose a three-layered data warehouse architecture: The back-end layer consists of a database management system for data collection, de-identification, and anonymisation of the original datasets. The role-based permissions and secured views are implemented in the access control layer. Lastly, the controller layer regulates the data access protocols for any data access and data analysis. We further present the data representation methods and the storage models considering the privacy and security mechanisms. The data privacy and security plans are devised based on the types of collected personal, the types of users, data storage, data transmission, and data analysis. We discuss in detail the challenges of privacy protection in this large distributed data-driven application and implement novel privacy-aware data analysis protocols to ensure that the proposed models guarantee the privacy and security of datasets. Finally, we present the BigO system architecture and its implementation that integrates privacy-aware protocols.

A Model for Enhancing Unstructured Big Data Warehouse Execution Time

“…A data lake combined with data wrangling provides a scalable platform for storing and analyzing huge volumes of research data, turning various data kinds and formats into structured data without the need for programming. In [46], the author describes the design of the BigO system, which collects large-scale data from children using sensor technologies in order to construct obesity prevalence models for data-driven predictions concerning particular policies at the community level. The paper suggests a three-layered data warehouse architecture for the proposed system, comprising a back-end layer for data collection, an access control layer with role-based permissions, and a controller layer that oversees data access protocols [20].…”

Harnessing Big Data Analytics for Healthcare: A Comprehensive Review of Frameworks, Implications, Applications, and Impacts

“…• Data protection laws [55], [61] • Privacy-preserving methods in big data [68], [132] --De-identification [18] --HybrEx [41], [54] --Identity-based anonymization [133] V…”

Secure Emergency Data Sharing in Healthcare - A Data Warehouse View

“…Views built only for query convenience, such as views developed to ease queries for which users do not need to comprehend the underlying data format, should not be utilized as secure views may take longer to perform than non-secure views. Shahid et al [15] break down into three types of secured views; statistical view giving measurements for characteristics that are automatically calculated, such as standard deviations, domain ranges, and value statistics; and anonymized view providing a comprehensive view of shared datasets protected by several techniques, and anatomized view providing both broad and detailed views of quasi-identifiers. Murphy [16] looks into an approach where data is frequently exported into a user-friendly data mart.…”