Secure statistical databases with random sample queries

By D. Denning. 1980

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Citations in the corpus

On the Difficulties of Disclosure Prevention in Statistical Databases or The Case for Differential Privacy (2010, neutral): “Although the effort seems to have been abandoned for over two decades, the work of Denning [10] is closest in spirit to the line of research recently pursued in [17, 4, 16].”
Calibrating Noise to Sensitivity in Private Data Analysis (2006, methodology): “The " classical " antecedent closest in spirit to our approach is the work of Denning [9].”
Database security - concepts, approaches, and challenges (2005, background): “Thus, relevant access modes include: select (to retrieve tuples from a table), insert (to add tuples to a table), delete (to remove tuples from a table), and update (to modify tuples in a table).”
L-diversity: privacy beyond k-anonymity (2006, unknown): “Techniques here include returning answers over a sample of the database [ Denning 1980 ], rounding the answers to a multiple of a prespecified base b [Dalenius 1981], and adding random noise to the outputs [Beck 1980].”
On the Utility of Privacy-Preserving Histograms (2004, background): “There is also mention of interactive solutions, such as query auditing [26] (see also [14] for a pessimistic view) and output perturbation ([13] [8], and, the more careful modern treatment [14] [15]).”
L-diversity: Privacy beyond k-anonymity (2007, unknown): “Techniques include returning answers over a sample of the database [Denning 1980], rounding the answers to a multiple of a prespecified base b [Dalenius 1981], and adding random noise to the outputs [Beck 1980].”
On Privacy-Preserving Histograms (2005, background): “There is also mention of interactive solutions, such as query auditing and output perturbation ([5], and, the more careful modern treatment [6]).”
Differential Privacy (2006, unknown): “Although the eﬀort seems to have been abandoned for over two decades, the work of Den-ning [7] is closest in spirit to the line of research recently pursued in [13, 3, 12].”
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”
Hippocratic Databases (2002, background): “The perturbation family includes swapping values between records [12], replacing the original database by a sample from the same distribution [33] [42], adding noise to the values in the database [52] [57], adding noise to the results of a query [4], and sampling the result of a query [11].”
Practical privacy: the SuLQ framework (2005, methodology): “In a related vein Denning [5] suggested choosing a random subset of the database and applying the query to the subset; we discuss this variant presently.”
Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms (2008, background): “This is commonly achieved by sampling the query results [31] or by introducing a varying perturbation (not permanent) to the data that are used to compute the result of a given query [32].”
Toward Privacy in Public Databases (2005, background): “A related approach is output perturbation, in which a query control mechanism receives queries, computes exact answers, and then outputs a perturbed answer as the response to the query [9, 5].”
A firm foundation for private data analysis (2011, methodology): “This broad category encompasses a generalization of subsampling, in which the curator first chooses, based on a secret, random, function of the query, a subsample from the database, and then returns the result obtained by applying the query to the subsample [4].”