Anonymization refers to the different techniques used to de-identify datasets, that is, to make sure that subjects in a dataset cannot be reidentified. Traditional anonymization techniques such as adding noise or grouping data retains a 1:1 link to original data subjects and re-identification risk remains. Behavioral or time-series data is especially hard to anonymize due to the unique patterns of the chronologically ordered events described by such datasets.