用于隐私保护的风险有效贝叶斯数据合成
Risk-Efficient Bayesian Data Synthesis for Privacy Protection
摘要：
统计机构利用模型来综合答复者一级的数据，以便向公众发布，以保护隐私。在这项工作中，我们有效地诱导隐私保护到任何贝叶斯合成模型，通过采用伪似然指数每个可能性贡献的观察记录索引的权重在[0，1]，定义为成反比的识别风险的记录。我们从记录的识别风险的边际概率开始，它由记录的身份可能被披露的概率组成。我们对美国劳工统计局消费者支出调查（CE）的应用表明，轻微风险调整的合成器提供了整体改善的隐私保护;然而，由于加权后隔离度增加，在风险调整的伪后验估计合成后，一些中等风险的记录的识别风险实际上增加了;我们将这种现象称为“打地鼠”。我们继续从与其他记录的成对识别风险概率的集合中为每个记录构建权重，其中每个成对概率测度重新识别该对记录的联合概率，这减轻了打地鼠问题，并产生了一组更有效的合成数据，其对于CE数据具有更低的风险和更高的效用。
英文摘要：
Statistical agencies utilize models to synthesize respondent-level data for release to the public for privacy protection. In this work, we efficiently induce privacy protection into any Bayesian synthesis model by employing a pseudo likelihood that exponentiates each likelihood contribution by an observation record-indexed weight in [0, 1], defined to be inversely proportional to the identification risk for that record. We start with the marginal probability of identification risk for a record, which is composed as the probability that the identity of the record may be disclosed. Our application to the Consumer Expenditure Surveys (CE) of the U.S. Bureau of Labor Statistics demonstrates that the marginally risk-adjusted synthesizer provides an overall improved privacy protection; however, the identification risks actually increase for some moderate-risk records after risk-adjusted pseudo posterior estimation synthesis due to increased isolation after weighting; a phenomenon we label "whack-a-mole". We proceed to construct a weight for each record from a collection of pairwise identification risk probabilities with other records, where each pairwise probability measures the joint probability of re-identification of the pair of records, which mitigates the whack-a-mole issue and produces a more efficient set of synthetic data with lower risk and higher utility for the CE data.
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