论文标题
使用精确的集群位置和其他隐私保护措施释放调查微数据
Releasing survey microdata with exact cluster locations and additional privacy safeguards
论文作者
论文摘要
世界各地的家庭调查计划发布了细粒状地理学的微数据，以支持有关人类生计及其周围环境的相互依赖性的研究。为了保护受访者的隐私，通常通过删除或扰动过程（例如混淆数据收集的真实位置）来匿名的微观调查数据。但是，这对新兴方法提出了一个挑战，可以通过有关地方一级的辅助信息来增强调查数据。在这里，我们提出了一种替代的微型数据传播策略，该策略通过使用生成模型通过合成生成的数据来利用原始微数据的实用性，并通过额外的隐私保护措施。我们使用2011年哥斯达黎加人口普查和卫星衍生的辅助信息的数据来支持我们的建议。我们的策略即使在重新识别尝试下，受访者对任何数量披露的属性的重新确定风险也会减少60-80 \％。
Household survey programs around the world publish fine-granular georeferenced microdata to support research on the interdependence of human livelihoods and their surrounding environment. To safeguard the respondents' privacy, micro-level survey data is usually (pseudo)-anonymized through deletion or perturbation procedures such as obfuscating the true location of data collection. This, however, poses a challenge to emerging approaches that augment survey data with auxiliary information on a local level. Here, we propose an alternative microdata dissemination strategy that leverages the utility of the original microdata with additional privacy safeguards through synthetically generated data using generative models. We back our proposal with experiments using data from the 2011 Costa Rican census and satellite-derived auxiliary information. Our strategy reduces the respondents' re-identification risk for any number of disclosed attributes by 60-80\% even under re-identification attempts.
