Security-control methods for statistical databases: a comparative study

By N. Adam, J. Wortmann. 1989

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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): “See [1] for a survey of many techniques developed prior to 1989.”
Differential Privacy via Wavelet Transforms (2009, background): “A plethora of techniques have been proposed for privacy-preserving data publishing (see [1], [2] for surveys).”
Smooth sensitivity and sampling in private data analysis (2007, background): “That is, the noise magnitude is determined not only by the function we want to release, but also by the database itself.”
Personalized privacy preservation (2006, methodology): “Figure 9 shows the execution time of our algorithm (Figure 4) in producing the generalized tables used in the experi- ments of Figure 8, as a function of ws.”
Privacy-Preserving Datamining on Vertically Partitioned Databases (2004, methodology): “Then Pr[â`|di = σ, d{−i}] = 2 Pr[â`|di = σ1, d{−i}] where |E[2]| = O(1/R) and 2 = {−(−1)g`(σ1)O(log2 n/√R) if E ≤ 0 (−1)g`(σ1)O(log(2) n/√R) if E > 0 and E is noise that yields â` when di = σ.”
Preventing Location-Based Identity Inference in Anonymous Spatial Queries (2007, methodology): “Adam and Wortmann [1] survey methods for computing aggregate functions (for example, sum and count ), under the condition that the results do not reveal any specific record.”
Robust De-anonymization of Large Sparse Datasets (2008, background): “4Increasing φ increases the false negative rate, i.e., the chance of erroneously dismissing a correct match, and decreases the false positive rate; φ may be chosen so that the two rates are equal. assumptions about the distribution from which the data are drawn.”
Aggregate Query Answering on Anonymized Tables (2007, background): “In perturbation-based approaches [1, 4, 6], noise following a certain distribution is added to sensitive attribute of each tuple.”
Privacy-preserving data mining: why, how, and when (2004, background): “• Statistical queries.(14) A statistical database aims to support queries that give summary results, yet prevent the determination of exact values in the database.”
Injecting utility into anonymized datasets (2006, background): “Note that there are other ways of sanitizing data: tuple suppression [31], adding random noise [5, 16, 1], and swapping attributes between tuples [12].”
Privacy-preserving data publishing: A survey of recent developments (2010, methodology): “There are some decent surveys [Adam and Wortman 1989; Domingo-Ferrer 2001; Moore 1996; Zayatz 2007] in the statistics community.”
PriPeARL: A Framework for Privacy-Preserving Analytics and Reporting at LinkedIn (2018, background): “This is due to the effect of suppression to 0 for most of the true counts (which mostly belong to the range [1,10]).”