Local Processing to Achieve Anonymity in a Participatory Health e-Research System

The use of participatory sensing in health e-Research applications is rapidly becoming a possibility due to the adoption of mobile computing technologies and sensing platforms. Such a change will have important benefits in the access to near real- time, large- scale up to population-wide data collection and analysis. However, there are numerous issues implied. Primarily of concern is how to ensure anonymity and privacy within these methodologies, and further the related issue of how to incentivize participants and remove barriers/concerns over participation. To address these concerns, in this paper we introduce a novel system to capture aggregate population health research data via utilizing smartphone capabilities while fully maintaining the anonymity and privacy of each individual contributing such data. A key and novel capability of this system is the support for customizable data collection; without the need to know specific details about an individual. The customized collection rules can be deployed on the local device based on detailed local data, and the resultant collection can be measured by the anonymous data collection network. In this paper we provide a conceptual architecture and describe a method for local processing of aggregate e-Research health data utilizing adaptive privacy thresholds to create a multi-party flexible approach to participatory data submission to support this novel health e-Research capability.