Abstract
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 gossips. We present Lohpi as a novel architecture for research data processing and evaluate the dissemination, overhead, and fault-tolerance.
Highlights
Data-driven research on human subjects often requires informed consent from participating individuals before data can be collected and processed (Schneider, 2019)
Goodman and Meslin (2014) and Salerno et al (2017) highlight the challenges and goals for data sharing in epidemiology in particular. This includes the problems we find in different areas of big-data computing on human data, including informed consent, individual privacy, harm, and data re-identification
We argue that through regular compliance checks, an ethics committee can identify and mitigate privacy risks that may exist in some projects
Summary
Data-driven research on human subjects often requires informed consent from participating individuals before data can be collected and processed (Schneider, 2019). The existing differential privacy studies assume a simple data model and centralized database They are not feasible for already collected research data that lies in federated databases at multiple trustworthy research. Salerno et al discuss the ethics in computing in the context of big data, epidemiology, and public health They discuss concerns where multiple data sources can be linked without a subject’s informed consent, which can result in re-identification of the subject. It includes changes to people who have access to the collected data (new researchers), newly discovered risks for subjects (new threats), and even changes in conditions for dispensation from professional secrecy requirements (new laws). These changes need to be approved by an ethics committee. We are building Lohpi as a platform for compliant data usage among researchers, which might identify a rogue researcher (Camden, 2005)
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