量化公共统计数据对保障性住房居民的隐私风险
Quantifying Privacy Risks of Public Statistics to Residents of Subsidized Housing
AI总结：
针对美国公共统计数据的隐私保护争议，通过结合人口普查和住房部数据，展示了可识别2010年违规保障性住房家庭的低成本重构攻击，并验证了类似人口普查局2020年差分隐私机制的防护效果，为政策制定者提供参考。
摘要：
随着美国人口普查局实施其颇具争议的新披露规避系统，研究人员和政策制定者就公共统计数据新隐私保护措施的必要性展开辩论。我们通过对公共统计数据和合成微观数据的实验，探讨了一个特定隐私问题：保障性住房的受访者可能因害怕被发现和驱逐，故意不提及未登记儿童和其他家庭成员。通过结合十年一度人口普查和住房与城市发展部的公共统计数据，我们展示了一种简单、低成本的重构攻击，该攻击可识别2010年违反居住指南的保障性住房家庭。对合成数据的实验表明，与人口普查局2010年披露规避措施类似的随机交换机制并未显著降低该攻击的精度，而与2020年披露规避系统类似的差分隐私机制则能降低精度。我们的结果为寻求可信公共统计数据的政策制定者提供了宝贵案例。
英文摘要：
As the U.S. Census Bureau implements its controversial new disclosure avoidance system, researchers and policymakers debate the necessity of new privacy protections for public statistics. With experiments on both public statistics and synthetic microdata, we explore a particular privacy concern: respondents in subsidized housing may deliberately not mention unauthorized children and other household members for fear of being discovered and evicted. By combining public statistics from the Decennial Census and the Department of Housing and Urban Development, we demonstrate a simple, inexpensive reconstruction attack that could identify subsidized households living in violation of occupancy guidelines in 2010. Experiments on synthetic data suggest that a random swapping mechanism similar to the Census Bureau's 2010 disclosure avoidance measures does not significantly reduce the precision of this attack, while a differentially private mechanism similar to the 2020 disclosure avoidance system does. Our results provide a valuable example for policymakers seeking trustworthy public statistics.
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