Sharma Aakash, Nilsen Thomas Bye, Czerwinska Katja Pauline, Onitiu Daria, Brenna Lars, Johansen Dag, Johansen Håvard D
Department of Computer Science, UiT The Arctic University of Norway, Tromsø, Norway.
Faculty of Design, Computer Science, Media, RheinMain University of Applied Sciences, Wiesbaden, Germany.
Front Big Data. 2021 May 13;4:624424. doi: 10.3389/fdata.2021.624424. eCollection 2021.
Researchers and researched populations are actively involved in participatory epidemiology. Such studies collect many details about an individual. Recent developments in statistical inferences can lead to sensitive information leaks from seemingly insensitive data about individuals. Typical safeguarding mechanisms are vetted by ethics committees; however, the attack models are constantly evolving. Newly discovered threats, change in applicable laws or an individual's perception can raise concerns that affect the study. Addressing these concerns is imperative to maintain trust with the researched population. We are implementing Lohpi: an infrastructure for building accountability in data processing for participatory epidemiology. We address the challenge of data-ownership by allowing institutions to host data on their managed servers while being part of Lohpi. We update data access policies using . We present Lohpi as a novel architecture for research data processing and evaluate the dissemination, overhead, and fault-tolerance.
研究人员和被研究人群积极参与参与式流行病学研究。此类研究收集有关个体的许多细节。统计推断的最新进展可能导致从看似不敏感的个体数据中泄露敏感信息。典型的保障机制需经伦理委员会审核;然而,攻击模式在不断演变。新发现的威胁、适用法律的变化或个人认知的改变都可能引发影响研究的担忧。解决这些担忧对于维持与被研究人群的信任至关重要。我们正在实施Lohpi:一种用于在参与式流行病学数据处理中建立问责制的基础设施。我们通过允许机构在其管理的服务器上托管数据同时作为Lohpi的一部分来解决数据所有权问题。我们使用 来更新数据访问策略。我们将Lohpi作为一种新颖的研究数据处理架构进行展示,并评估其传播性、开销和容错能力。