Disclosure-Limited Data Dissemination

By G. Duncan, Diane Lambert. 1986

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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.”
Transforming data to satisfy privacy constraints (2002, background): “An important issue that has to be addressed is the protection of the privacy of individuals or entities referred to in the released micro-data [19, 20, 6].”
Differentially Private Filtering (2012, methodology): “We refer the reader to the surveys by Dwork, e.g., [17], for additional background on differential privacy.”
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].”
Estimation of Treatment Effects from Combined Data: Identification versus Data Security (2015, unknown): “Duncan and Lambert (1986), Duncan and Mukherjee (1991), Duncan and Pearson (1991), Fienberg (1994), and Fienberg (2001) Duncan, Fienberg, Krishnan, Padman, and Roehrig (2001), Abowd and Woodcock (2001) show that synthetic data may be a useful tool in the analysis of particular distributional…”
Economic Analysis and Statistical Disclosure Limitation (2016, methodology): “This concept, known as inferential disclosure, was introduced by Dalenius (1977) and formalized by Duncan and Lambert (1986) in statistics, and by Goldwasser and Micali (1982) in computer science.”
t-Closeness: Privacy Beyond k-Anonymity and l-Diversity (2007, background): “Two types of information disclosure have been identified in the literature [4, 9]: identity disclosure and attribute disclosure.”
Privacy: Theory meets Practice on the Map (2008, background): “These include general notions of statistical closeness [5], variants of the notions of k-anonymity [6] and l-diversity [7], [8], [9], [10], [11], (pl,p2)-privacy [12], and variants of differential privacy [13], [14].”
Local Privacy and Statistical Minimax Rates (2013, background): “A major challenge in statistical inference is that of characterizing and controlling the balance between statistical efficiency and the privacy of individuals from whom data is obtained [9, 11, 14].”
A measure of disclosure risk for microdata (2002, background): “Duncan and Lambert (1986, 1989), Paass (1988), Lambert (1993), Fuller (1993), Skinner et al.”
A Framework for Evaluating the Utility of Data Altered to Protect Confidentiality (2006, background): “Confidence intervals are the main mechanism of inference in regression models.”
On the tradeoff between privacy and utility in data publishing (2009, background): “Several types of information disclosure in microdata publishing have been identified in the literature [6, 16].”
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].”
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).”