On the Question of Statistical Confidentiality

By I. Fellegi. 1972

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Citations in the corpus

Efficient k -Anonymization Using Clustering Techniques (2007, methodology): “Another important technique is query restriction [7, 9 ], which restricts queries that may result in inference.”
Assessing global disclosure risk in masked microdata (2004, background): “Other countries have promulgated similar privacy regulations (for example, the Canadian Standard Association s Model Code for the Protection of Personal Information [33] and the Australian Privacy Amendment Act 2000 [3]).”
k-Anonymity: A Model for Protecting Privacy (2002, background): “Federal and state statistics offices around the world have traditionally been entrusted with the release of statistical information about all aspects of the populace [5].”
Revealing information while preserving privacy (2003, background): “Methods of output perturbation include varying output perturbations [6], where a random perturbation is added to the query answer, with increasing variance as the query is repeated, and rounding - either deterministic [1] or probabilistic [12].”
L-diversity: privacy beyond k-anonymity (2006, unknown): “Census data literature focuses on identifying and protecting the privacy of sensitive entries in contingency tables – tables of counts which represent the complete cross-classification of the data ([ Fellegi 1972; Cox 1980; 1982; 1987; Dobra and Feinberg 2003; 2000; Slavkovic and Feinberg 2004]).”
Hippocratic Databases (2002, background): “The query restriction family includes restricting the size of query results [13] [18], controlling the overlap among successive queries [14], keeping audit trails of all answered queries and constantly checking for possible compromises [8], suppression of data cells of small size [9], and clustering”
An Ad Omnia Approach to Defining and Achieving Private Data Analysis (2007, background): “There is a rich literature on this problem, principally from the satistics community [11, 15, 24, 25, 26, 34, 36, 23, 35] (see also the literature on controlled release of tabular data, contingency tables, and cell suppression), and from such diverse branches of computer science as algorithms, datab”
Privacy, accuracy, and consistency too: a holistic solution to contingency table release (2007, background): “Privacy, Accuracy, and Consistency Too: A Holistic Solution to Contingency Table Release * Boaz Barak Princeton University boaz@cs.princeton.edu * Satyen Kale Princeton University satyen@cs.princeton.edu * Kamalika Chaudhuri UC Berkeley kamalika@cs.berkeley.edu Frank McSherry Microsoft Research,”
Risk‐Utility Paradigms for Statistical Disclosure Limitation: How to Think, But Not How to Act (2011, methodology): “An earlier methodology for tabular SDL addressing only exact disclosure of sensitive cell values was provided by Fellegi (1972).”
Differential Privacy: A Survey of Results (2008, unknown): “There is a rich literature on this problem, principally from the satistics community (see, e.g., [10, 14, 27, 28, 29, 38, 40, 26 , 39] and the literature on controlled”
Information sharing across private databases (2003, background): “These techniques include restricting the size of query results [17, 23], controlling the overlap among successive queries [19], and keeping audit trails of all answered queries to detect possible compromises [13].”
Economic Analysis and Statistical Disclosure Limitation (2016, background): “From the outset, it was understood that primary suppression–not publishing easily identified data items–does not protect anything if the agency publishes the rest of the data, including summary statistics (Fellegi 1972).”
L-diversity: Privacy beyond k-anonymity (2007, unknown): “Census data literature focuses on identifying and protecting the privacy of sensitive entries in contingency tables, tables of counts which represent the complete cross-classi.cation of the data ([Fellegi 1972; Cox 1980; 1982; 1987; Dobra and Feinberg 2003; 2000; Slavkovic and Feinberg 2004]).”