A Provenance Model for Quantified Self Data

Abstract

Quantified Self became popular in recent years. People are tracking themselves with Wearables, smartphone apps, or desktop applications. They collect, process and store huge amounts of personal data for medical and other reasons. Due to the complexity of different data sources, apps, and cloud services, it is hard to follow the data flow and to have trust in data integrity and safety. We present a solution that helps to get insight in Quantified Self data flows and to answer questions related to data security. We provide a provenance model for Quantified Self data based on the W3C standard PROV. Using that model, developers and users can record provenance of Quantified Self apps and services with a standardized notation. We show the feasibility of the presented provenance model with a small workflow using steps data from Fitbit fitness tracker.