A framework for evaluating the utility of data altered to protect confidentiality
A framework for evaluating the utility of data altered to protect confidentiality
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DOI：
10.1198/000313006x124640
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发表时间：
2006-08-01
影响因子：
1.8
通讯作者：
Sanil, A. P.
中科院分区：
文献类型：
--
作者：
Karr, A. F.;Kohnen, C. N.;Sanil, A. P.
在向公众公布数据时，统计机构和调查组织通常会更改数据值，以保护调查对象身份和属性值的机密性。为了在各种各样的数据更改方法中进行选择，各机构需要评估拟议数据发布效用的工具。这种效用衡量可以与披露风险衡量相结合，以衡量竞争方法的风险-效用权衡。本文介绍了效用的措施，重点是从修改后的数据和相应的推断得到的推断的差异。原始数据。使用真实的和模拟的数据，我们展示了如何可以使用的措施，在决策理论制定评估披露限制程序。
When releasing data to the public, statistical agencies and survey organizations typically alter data values in order to protect the confidentiality of survey respondents' identities and attribute values. To select among the wide variety of data alteration methods, agencies require tools for evaluating the utility of proposed data releases. Such utility measures can be combined with disclosure risk measures to gauge risk-utility tradeoffs of competing methods. This article presents utility measures focused on differences in inferences obtained from the altered data and corresponding inferences obtained from. the original data. Using both genuine and simulated data, we show how the measures can be used in a decision-theoretic formulation for evaluating disclosure limitation procedures.