Chapter title |
When Excessive Perturbation Goes Wrong and Why IPUMS-International Relies Instead on Sampling, Suppression, Swapping, and Other Minimally Harmful Methods to Protect Privacy of Census Microdata
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Chapter number | 14 |
Book title |
Privacy in Statistical Databases
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Published in |
Privacy in statistical databases : UNESCO Chair in Data Privacy, International Conference, PSD 2012, Palermo, Italy, September 26-28, 2012. Proceedings. PSD (Conference : 2004-) (2012 : Palermo, Italy), September 2012
|
DOI | 10.1007/978-3-642-33627-0_14 |
Pubmed ID | |
Book ISBNs |
978-3-64-233626-3, 978-3-64-233627-0
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Authors |
Lara Cleveland, Robert McCaa, Steven Ruggles, Matthew Sobek |
Abstract |
IPUMS-International disseminates population census microdata at no cost for 69 countries. Currently, a series of 212 samples totaling almost a half billion person records are available to researchers. Registration is required for researchers to gain access to the microdata. Statistics from Google Analytics show that IPUMS-International's lengthy, probing registration form is an effective deterrent for unqualified applicants. To protect data privacy, we rely principally on sampling, suppression of geographic detail, swapping of records across geographic boundaries, and other minimally harmful methods such as top and bottom coding. We do not use excessively perturbative methods. A recent case of perturbation gone wrong- the household samples of the 2000 census of the USA (PUMS), the 2003-2006 American Community Survey, and the 2004-2009 Current Population Survey-, an empirical study of the impact of perturbation on the usability of UK census microdata-the Individual SARs of the 1991 census of the UK-, and a mathematical demonstration in a timely compendium of statistical confidentiality practices confirm the wisdom of IPUMS microdata management protocols and statistical disclosure controls. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 14 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 4 | 29% |
Other | 3 | 21% |
Professor > Associate Professor | 2 | 14% |
Student > Master | 2 | 14% |
Student > Ph. D. Student | 1 | 7% |
Other | 0 | 0% |
Unknown | 2 | 14% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 5 | 36% |
Social Sciences | 4 | 29% |
Mathematics | 1 | 7% |
Arts and Humanities | 1 | 7% |
Economics, Econometrics and Finance | 1 | 7% |
Other | 1 | 7% |
Unknown | 1 | 7% |