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When releasing record-level data containing sensitive information to thepublic, the data disseminator is responsible for protecting the privacy ofevery record in the dataset, simultaneously preserving important features ofthe data for users' analyses. These goals can be achieved by data synthesis,where confidential data are replaced with synthetic data that are simulatedbased on statistical models estimated on the confidential data. In this paper,we present a data synthesis case study, where synthetic values of price and thenumber of available days in a sample of the New York Airbnb Open Data arecreated for privacy protection. One sensitive variable, the number of availabledays of an Airbnb listing, has a large amount of zero-valued records and alsotruncated at the two ends. We propose a zero-inflated truncated Poissonregression model for its synthesis. We utilize a sequential synthesis approachto further synthesize the sensitive price variable. The resulting syntheticdata are evaluated for its utility preservation and privacy protection, thelatter in the form of disclosure risks. Furthermore, we propose methods toinvestigate how uncertainties in intruder's knowledge would influence theidentification disclosure risks of the synthetic data. In particular, weexplore several realistic scenarios of uncertainties in intruder's knowledge ofavailable information and evaluate their impacts on the resultingidentification disclosure risks.

Data Privacy Protection and Utility Preservation through Bayesian Data Synthesis: A Case Study on Airbnb Listings