The Risk of Disclosure for Microdata
By G. Duncan, Diane Lambert. 1989
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Michael I. Jordan
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- Ruslan Salakhutdinov
- Percy Liang
- Martin J. Wainwright
- Sergey Levine
- Francis R. Bach
Citations in the corpus
- Minimax Optimal Procedures for Locally Private Estimation (2016, background): “A major challenge in statistical inference is that of characterizing and balancing statistical utility with the privacy of individuals from whom data are obtained (Duncan and Lambert 1986, 1989; Fienberg, Makov, and Steele 1998).”
- Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation (2013, background): “In the large body of research on privacy and statistical inference [e.g., 27, 18, 12, 13, 19], a major focus has been on the problem of reducing disclosure risk: the probability that a member of a dataset can be identified given released statistics of the dataset.”
- Estimating Risks of Identification Disclosure in Microdata (2005, methodology): “…(Paass 1988; Blien, Wirth, and Muller 1992; Federal Committee on Statistical Methodology 1994; Yancey, Winkler, and Creecy 2002) or indirect matching using the existing database (Spruill 1982; Duncan and Lambert 1986, 1989; Lambert 1993; Fienberg, Makov, and Sanil 1997; Skinner and Elliot 2002).”
- Local Privacy, Data Processing Inequalities, and Statistical Minimax Rates (2013, background): “A major challenge in statistical inference is that of characterizing and balancing statistical utility with the privacy of individuals from whom data is obtained [20, 21, 28].”
- A ug 2 01 4 Local Privacy , Data Processing Inequalities , and Minimax Rates (2018, background): “A major challenge in statistical inference is that of characterizing and balancing statistical utility with the privacy of individuals from whom data is obtained [20, 21, 28].”
- A Framework for Evaluating the Utility of Data Altered to Protect Confidentiality (2006, background): “While there has been much work on developing measures of disclosure risk (e.g., Duncan and Lambert, 1986, 1989; Lambert, 1993; Fienberg et al., 1997; Skinner and Elliot, 2002; Reiter, 2005a), there has been comparatively little work on developing measures of data utility, and so this paper outline”