k-Anonymity on Metagenomic Features in Microbiome Databases

The human microbiome is increasingly subject to extensive research, due to its relations to health, diet, exercise and illness. While ever more microbiome data is gathered and stored, recent works have demonstrated the threat of individual re-identification based on matching samples taken at different points in time, by matching metagenomic features extracted from microbiome readings. The individual and temporal stability of the microbiome varies for different body sites and is particularly pronounced for readings from the gastrointestinal tract. To meet the resulting need for privacy-protecting solutions, we adapt the well-known concept of k-anonymity and make it suitable for application to microbiome datasets. In particular, our approach for establishing k-anonymity is based on micro-aggregation.Our evaluation uses ten datasets containing samples of gut microbiomes, and analyzes the decreased privacy risk on the anonymised dataset as well as the incurred information loss. The analysis demonstrates the suitability of our approach for the protection of sensitive microbiome data.