  Guaranteeing privacy in released data is an important goal for data-producing
agencies. There has been extensive research on developing suitable privacy
mechanisms in recent years. Particularly notable is the idea of noise addition
with the guarantee of differential privacy. There are, however, concerns about
compromising data utility when very stringent privacy mechanisms are applied.
Such compromises can be quite stark in correlated data, such as time series
data. Adding white noise to a stochastic process may significantly change the
correlation structure, a facet of the process that is essential to optimal
prediction. We propose the use of all-pass filtering as a privacy mechanism for
regularly sampled time series data, showing that this procedure preserves
utility while also providing sufficient privacy guarantees to entity-level time
series.
