A Framework for Evaluating the Utility of Data Altered to Protect Confidentiality

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Privacy Aware Learning (2012, background): “Most work on privacy attempts to limit disclosure risk: the probability that some adversary can link a released record to a particular member of the population or identify that someone belongs to a dataset that generates a statistic [Duncan and Lambert 1986, 1989; Reiter 2005; Karr et al. 2006].”
Risk‐Utility Paradigms for Statistical Disclosure Limitation: How to Think, But Not How to Act (2011, methodology): “To illustrate, suppose that the original database O contains only numerical attributes and that the SDL consists of adding normally distributed noise, as in Karr et al. (2006a) and Oganian and Karr (2006).”
Privacy-Preserving Analysis of Vertically Partitioned Data Using Secure Matrix Products (2009, methodology): “Underlying these methods are quantified measures of data utility and disclosure risk (for individual records) (Karr et al. 2006a).”
Economic Analysis and Statistical Disclosure Limitation (2016, methodology): “Their analysis was based on the efforts of statisticians to explicitly model the trade-off between confidentiality risk and data usefulness (Duncan and Fienberg 1999; Karr et al. 2006).”