  Immune repertoires provide a unique fingerprint reflecting the immune history
of individuals, with potential applications in precision medicine. However, the
question of how personal that information is and how it can be used to identify
individuals has not been explored. Here, we show that individuals can be
uniquely identified from repertoires of just a few thousands lymphocytes. We
present "Immprint," a classifier using an information-theoretic measure of
repertoire similarity to distinguish pairs of repertoire samples coming from
the same versus different individuals. Using published T-cell receptor
repertoires and statistical modeling, we tested its ability to identify
individuals with great accuracy, including identical twins, by computing false
positive and false negative rates $< 10^{-6}$ from samples composed of 10,000
T-cells. We verified through longitudinal datasets and simulations that the
method is robust to acute infections and the passage of time. These results
emphasize the private and personal nature of repertoire data.
