Conference
Privacy in Statistical Databases
About: Privacy in Statistical Databases is an academic conference. The conference publishes majorly in the area(s): Synthetic data & Computer science. Over the lifetime, 268 publications have been published by the conference receiving 19733 citations.
Papers
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09 Jun 2004
TL;DR: This paper revisits the original (1982) published version of the Dalenius-Reiss data swapping paper and then traces the developments of statistical disclosure limitation methods that can be thought of as rooted in the original concept.
Abstract: Data swapping, a term introduced in 1978 by Dalenius and Reiss for a new method of statistical disclosure protection in confidential data bases, has taken on new meanings and been linked to new statistical methodologies over the intervening twenty-five years. This paper revisits the original (1982) published version of the the Dalenius-Reiss data swapping paper and then traces the developments of statistical disclosure limitation methods that can be thought of as rooted in the original concept. The emphasis here, as in the original contribution, is on both disclosure protection and the release of statistically usable data bases.
128 citations
22 Sep 2010
TL;DR: This work surveys current approaches to the record linkage problem in a privacy-aware setting and contrast these with the more traditional literature.
Abstract: Record linkage has a long tradition in both the statistical and the computer science literature. We survey current approaches to the record linkage problem in a privacy-aware setting and contrast these with the more traditional literature. We also identify several important open questions that pertain to private record linkage from different perspectives.
120 citations
24 Sep 2008
TL;DR: This short paper provides a synthesis of the statistical disclosure limitation and computer science data privacy approaches to measuring the confidentiality protections provided by fully synthetic data.
Abstract: This short paper provides a synthesis of the statistical disclosure limitation and computer science data privacy approaches to measuring the confidentiality protections provided by fully synthetic data. Since all elements of the data records in the release file derived from fully synthetic data are sampled from an appropriate probability distribution, they do not represent "real data," but there is still a disclosure risk. In SDL this risk is summarized by the inferential disclosure probability. In privacy-protected database queries, this risk is measured by the differential privacy ratio. The two are closely related. This result (not new) is demonstrated and examples are provided from recent work.
110 citations
09 Jun 2004
TL;DR: In this paper, a microaggregation procedure for categorical variables is proposed for protecting confidential data prior to their public release and compared with Top and Bottom Coding, Global recoding, Rank Swapping and PRAM.
Abstract: Microaggregation is a masking procedure used for protecting confidential data prior to their public release This technique, that relies on clustering and aggregation techniques, is solely used for numerical data In this work we introduce a microaggregation procedure for categorical variables We describe the new masking method and we analyse the results it obtains according to some indices found in the literature The method is compared with Top and Bottom Coding, Global recoding, Rank Swapping and PRAM
109 citations