Behavioral data, collected from our daily interactions with technology, have driven scientific advances. Yet, the collection and sharing of this data raise legitimate privacy concerns, as individuals can often be re-identified. Current identification attacks however
require auxiliary information to roughly match the information available in the dataset,
limiting their applicability. We here propose an entropy-based profiling model to learn
time-persistent profiles. Using auxiliary information about a single target collected over
a non-overlapping time period, we show that individuals are correctly identified 79% of
the time in a large location dataset of 0.5M individuals, and 65.2% for a grocery shopping
dataset of 85, 000 individuals. We further show that accuracy only slowly decreases over
time and that the model is robust to state-of-the-art noise addition. Our results show
that much more auxiliary information than previously believed can be used to identify
individuals, challenging de-identification practices and what currently constitutes legally
anonymous data.