The decreasing cost of molecular profiling tests, such as DNA sequencing, and the consequent increasing availability of biological data are revolutionizing medicine, but at the same time create novel privacy risks. The research community has already proposed a plethora of methods for protecting genomic data against these risks. However, the privacy risks stemming from epigenetics, which bridges the gap between the genome and our health characteristics, have been largely overlooked so far, even though epigenetic data such as microRNAs (miRNAs) are no less privacy sensitive. This lack of investigation is attributed to the common belief that the inherent temporal variability of miRNAs shields them from being tracked and linked over time.
In this paper, we show that, contrary to this belief, miRNA expression profiles can be successfully tracked over time, despite their variability. Specifically, we show that two blood-based miRNA expression profiles taken with a time difference of one week from the same person can be matched with a success rate of 90%. We furthermore observe that this success rate stays almost constant when the time difference is increased from one week to one year. In order to mitigate the linkability threat, we propose and thoroughly evaluate two countermeasures: (i) hiding a subset of disease-irrelevant miRNA expressions, and (ii) probabilistically sanitizing the miRNA expression profiles. Our experiments show that the second mechanism provides a better trade-off between privacy and disease-prediction accuracy.
