基于贝叶斯数据合成的数据隐私保护和效用维护--以Airbnb房源为例
Data Privacy Protection and Utility Preservation through Bayesian Data Synthesis: A Case Study on Airbnb Listings
摘要：
在向公众发布包含敏感信息的记录级数据时，数据传播者有责任保护数据集中每条记录的隐私，同时保留数据的重要特征供用户分析。这些目标可以通过数据合成来实现，其中机密数据被基于机密数据估计的统计模型模拟的合成数据替换。在本文中，我们提出了一个数据合成的案例研究，在纽约Airbnb开放数据的样本中创建了价格和可用天数的合成值，以保护隐私。一个敏感的变量，Airbnb房源的可用天数，有大量的零值记录，也在两端被截断。我们提出了一个零膨胀截断泊松回归模型的合成。我们利用一个连续的综合方法，进一步综合敏感的价格变量。对由此产生的合成数据进行效用保护和隐私保护评估，后者以披露风险的形式存在。此外，我们提出的方法来调查如何入侵者的知识的不确定性会影响识别披露风险的合成数据。特别是，我们探讨了几个现实的情况下，入侵者的知识，可用信息的不确定性，并评估其对所产生的识别披露风险的影响。
英文摘要：
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.
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