Motivated by privacy concerns in long-term longitudinal studies in medical
and social science research, we study the problem of continually releasing
differentially private synthetic data from longitudinal data collections. We
introduce a model where, in every time step, each individual reports a new data
element, and the goal of the synthesizer is to incrementally update a synthetic
dataset in a consistent way to capture a rich class of statistical properties.
We give continual synthetic data generation algorithms that preserve two basic
types of queries: fixed time window queries and cumulative time queries. We
show nearly tight upper bounds on the error rates of these algorithms and
demonstrate their empirical performance on realistically sized datasets from
the U.S. Census Bureau's Survey of Income and Program Participation
