  When releasing record-level data containing sensitive information to the
public, the data disseminator is responsible for protecting the privacy of
every record in the dataset, simultaneously preserving important features of
the data for users' analyses. These goals can be achieved by data synthesis,
where confidential data are replaced with synthetic data that are simulated
based on statistical models estimated on the confidential data. In this paper,
we present a data synthesis case study, where synthetic values of price and the
number of available days in a sample of the New York Airbnb Open Data are
created for privacy protection. One sensitive variable, the number of available
days of an Airbnb listing, has a large amount of zero-valued records and also
truncated at the two ends. We propose a zero-inflated truncated Poisson
regression model for its synthesis. We utilize a sequential synthesis approach
to further synthesize the sensitive price variable. The resulting synthetic
data are evaluated for its utility preservation and privacy protection, the
latter in the form of disclosure risks. Furthermore, we propose methods to
investigate how uncertainties in intruder's knowledge would influence the
identification disclosure risks of the synthetic data. In particular, we
explore several realistic scenarios of uncertainties in intruder's knowledge of
available information and evaluate their impacts on the resulting
identification disclosure risks.
