School of Computer Science, University College Dublin, Belfield, Dublin 4, Ireland.
Sensors (Basel). 2021 Mar 28;21(7):2353. doi: 10.3390/s21072353.
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.
肥胖是全球一个主要的公共卫生问题，儿童肥胖的流行尤其令人关注。预防和治疗儿童肥胖的有效干预措施旨在改变个人、社区和社会各级的行为和暴露。然而，监测和评估这些变化极具挑战性。欧盟地平线 2020 项目“大数据对抗儿童肥胖症（BigO）”旨在使用不同的传感器技术从大量儿童那里收集大规模数据，为社区特定政策创建全面的肥胖流行模型，以便进行数据驱动的预测。它还通过有意义的实时数据分析和可视化，为人口反应提供实时监测。由于 BigO 涉及监测和存储与潜在弱势群体行为相关的个人数据，因此数据表示、安全性和访问控制至关重要。在本文中，我们简要介绍了 BigO 系统架构，并重点介绍了处理数据访问控制、存储、匿名化以及与系统其余部分的相应接口的系统必要组件。我们提出了一个三层数据仓库架构：后端层由一个数据库管理系统组成，用于数据收集、原始数据集的去标识和匿名化。基于角色的权限和安全视图在访问控制层中实现。最后，控制器层调节任何数据访问和数据分析的数据访问协议。我们进一步介绍了考虑隐私和安全机制的数据表示方法和存储模型。基于收集的个人资料类型、用户类型、数据存储、数据传输和数据分析，制定了数据隐私和安全计划。我们详细讨论了在这个大型分布式数据驱动应用程序中隐私保护的挑战，并实施了新的隐私感知数据分析协议，以确保所提出的模型保证数据集的隐私和安全。最后，我们介绍了集成隐私感知协议的 BigO 系统架构及其实现。
