Distance-based Techniques for Personal Microbiome Identification✱

Abstract

Due to its high potential for analysis in clinical settings, research on the human microbiome has been flourishing for several years. As an increasing amount of data on the microbiome is gathered and stored, analysing the temporal and individual stability of microbiome readings and the ensuing privacy risks has gained importance. In 2015, Franzosa et al. demonstrated the feasibility of microbiome-based identifiability on datasets from the Human Microbiome Project, thus posing privacy implications for microbiome study designs. Their technique is based on the construction of body site-specific metagenomic codes that maintain a certain stability over time. In this paper, we establish a distance-based technique for personal microbiome identification, which is combined with a solution for avoiding spurious matches. In a direct comparison with the approach from Franzosa et al., our method improves upon the identification results on most of the considered datasets. Our main finding is an increase of the average percentage of true positive identifications of 30% on the widely studied microbiome of the gastrointestinal tract. While we particularly recommend our method for application on the gut microbiome, we also observed substantial identification success on other body sites. Our results demonstrate the potential of privacy threats in microbiome data gathering, storage, sharing, and analysis, and thus underline the need for solutions to protect the microbiome as personal and sensitive medical data.