Elements of Statistical Disclosure Control

By L. Willenborg, T. Waal. 2000

693 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Marketing

View Paper: https://www.semanticscholar.org/paper/eadd27685ed843e8a08bb6acbceabaf31653e21b

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Citations in the corpus

Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data (2017, methodology): “…be achieved by modifying the data to preserve degrees of anonymity (using techniques such as statistical disclosure control (Hundepool et al., 2012; Willenborg and de Waal, 2012), and privacy-preserving data mining (Agrawal and Srikant, 2000), which allow the statistical properties of the data to…”
Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation (2005, methodology): “There exist a plethora of methods to protect microdata in addition to the ones mentioned above (see Domingo-Ferrer and Torra (2005), Domingo-Ferrer and Torra (2001a) and Willenborg and DeWaal (2001).”
Incognito: efficient full-domain K-anonymity (2005, background): “Joining attacks are made easier by the availability of other, complementary, databases over the Internet.”
Outlier Protection in Continuous Microdata Masking (2004, background): “See [ 10 , 5, 11, 1] for a survey of such methods.”
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].”
A Survey of Inference Control Methods for Privacy-Preserving Data Mining (2008, methodology): “Examples are the passport number, social security number, namesurname, etc. Quasi-identifiers or key attributes.”
Secure Regression on Distributed Databases (2005, background): “This is the classical p -rule in the statistical disclosure limitation literature (Willenborg and de Waal, 2001).”
Releasing multiply imputed, synthetic public use microdata: an illustration and empirical study (2005, background): “This section spells out confidentiality–utility trades for fully synthetic data, with comparisons with alternative disclosure control techniques.”
Assessing global disclosure risk in masked microdata (2004, background): “…global and local recoding [27, 34, 40], suppression and local suppression [34, 26], microaggregation [13], simulation [1], adding noise [22], randomization or perturbation methods [28, 24, 29, 16], data swapping [9, 32], substitution [37] etc. have been extensively discussed in literature [45].”
Mondrian Multidimensional K-Anonymity (2006, background): “Numerous recoding models have been proposed in the literature for k-anonymization [8] [9] [13] [17] [10] Figure 1.”
Protecting privacy using k-anonymity. (2008, methodology): “56,57 However, a smart intruder would focus on the records in equivalence classes with the highest probability of re-identiﬁcation.”
Estimating Risks of Identification Disclosure in Microdata (2005, background): “…and many have been proposed, some based on squared or absolute differences in the sample means and covariance matrices between the released and original data (Duncan et al. 2001; Domingo-Ferrer and Torra 2001; Yancey et al. 2002) and others based on entropy measures (Willenborg and de Waal 2001).”