Anonymizing Temporal Data

Abstract

Temporal data are time-critical in that the snapshot at each timestamp must be made available to researchers in a timely fashion. However, due to the limited data, each snapshot likely has a skewed distribution on sensitive values, which renders classical anonymization methods not possible. In this work, we propose the “reposition model” to allow a record to be published within a close proximity of original timestamp. We show that reposition over a small proximity of timestamp is sufficient for reducing the skewness of a snapshot, therefore, minimizing the impact on window queries. We formalize the optimal reposition problem and present a linear-time solution. The contribution of this work is that it enables classical methods on temporal data.