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.

In human subject research, various data about the studied individuals are collected. Through re-identification and statistical inferences, this data can be exploited for interests other than the ones the subjects initially consented to. Such exploitation must be avoided to maintain trust with the researched population. We argue that keeping data-access policies up-to-date and building accountability on research data processing can reflect subjects' consent and mitigate data misuse. With accountability in mind, we are building Lohpi: a decentralized system for research data sharing with up-to-date access policies. We demonstrate our initial prototype with timely delivery of policy changes along with minimal access control overhead.

Data-driven research is increasingly becoming fueled by access to open datasets, often shared publicly on the Internet. However, many research projects study sensitive data. They cannot easily participate in this shift as access to their data is significantly controlled by ethical and regulatory constraints. This paper discusses the requirements for building a service that enables sensitive data for sharing between collaborators in a controlled manner. We argue that a decentralized service that maintains metadata, a global view on all data usage, and active policy combined with local monitoring and security enforcement can provide automated compliance checking. With such a service, researchers can share sensitive data with a broader community rather than limiting access to core project members.