software . 2019

Replication Package for "A Practical Method to Reduce Privacy Loss when Disclosing Statistics Based on Small Samples"

Chetty, Raj; Friedman, John N.;
  • Published: 08 Oct 2019
  • Publisher: Figshare
Abstract
<p>We develop a simple method to reduce privacy loss when disclosing statistics such as OLS regression estimates based on samples with small numbers of observations. We focus on the case where the dataset can be broken into many groups (&quot;cells&quot;) and one is interested in releasing statistics for one or more of these cells. Building on ideas from the differential privacy literature, we add noise to the statistic of interest in proportion to the statistic&#39;s maximum observed sensitivity, defined as the maximum change in the statistic from adding or removing a single observation across all the cells in the data. Intuitively, our approach permits the rel...
Subjects
free text keywords: Neuroscience, Biotechnology, Ecology, Space Science, 19999 Mathematical Sciences not elsewhere classified, formal privacy, privacy loss, statistical bias
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Software . 2019
Provider: Datacite
Zenodo
Software . 2019
Provider: Datacite
Zenodo
Software . 2019
Provider: Zenodo
figshare
Software . 2019
Provider: figshare
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software . 2019

Replication Package for "A Practical Method to Reduce Privacy Loss when Disclosing Statistics Based on Small Samples"

Chetty, Raj; Friedman, John N.;