Difference between revisions of "Statistical Disclosure Control"
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===Review Articles=== | ===Review Articles=== | ||
* Fienberg, Stephen, "Confidentiality and Disclosure Limitation," Encyclopedia of Social Measurement, Volume 1, 2005. '''A good overview article about statistical disclosure limitation, not too much math. No mention of differential privacy, of course.''' | * Fienberg, Stephen, "Confidentiality and Disclosure Limitation," Encyclopedia of Social Measurement, Volume 1, 2005. '''A good overview article about statistical disclosure limitation, not too much math. No mention of differential privacy, of course.''' | ||
===Critiques=== | |||
Many contemporary statistical disclosure control techniques, such as generalization and cell suppression, have been shown to be vulnerable to attack by a hypothetical data intruder. For example, Cox showed in 2009 that Complementary cell suppression typically leads to "over-protected" solutions because of the need to suppress both primary and complementary cells, and even then can lead to the compromise of sensitive data when exact intervals are reported.<ref>Lawrence H. Cox, Vulnerability of Complementary Cell Suppression to Intruder Attack, Journal of Privacy and Confidentiality (2009) 1, Number 2, pp. 235–251 http://repository.cmu.edu/cgi/viewcontent.cgi?article=1017</ref> | |||
Revision as of 14:01, 17 June 2017
Presentations
- Disclosure Avoidance Methods and Research at the U. S. Census Bureau, Billy Wisniewski, Amy Lauger, and Laura McKenna, Center for Disclosure Avoidance, Research (CDAR), U.S. Census Bureau, April 21, 2015
- eConfidentiality - a Disclosure Avoidance Application System (Proposed), Bei Wang, U.S. Census Bureau, April 21, 2015
Papers
US Census Bureau
- “Examples of Easy-to-implement, Widely Used Masking Methods for which Analytic Properties are not Justified,” Winkler, W. E. (2007b),
- Disclosure Avoidance Techniques at the U.S. Census Bureau: Current Practices and Research, Amy Lauger, Billy Wisniewski, and Laura McKenna, Center for Disclosure Avoidance Research, RESEARCH REPORT SERIES #2014-02, U.S. Census Bureau, September 26, 2014.
- Disclosure Avoidance Practices and Research at the U.S. Census Bureau: An Update, Laura Zayatz, Statistical Research Division, RESEARCH REPORT SERIES, (Statistics #2005-06), Revised August 31, 2005
- Disclosure, U.S. Census Bureau Economic Census website, Last Revised: March 05, 2015
- Disclosure Avoidance for Census 2010 and American Community Survey Five-year Tabular Data Products , Laura Zayatz, Jason Lucero, Paul Massell, Asoka Ramanayake, RESEARCH REPORT SERIES (Statistics #2009-10) , November 23, 2009
- A Monograph on Confidentiality and Privacy in the U.S. Census, George Gatewood, US Census Policy Office, July 2001.
- A Disclosure Avoidance Research Agenda, Paul B. Massell, Center for Disclosure Avoidance Research, U.S. Census Bureau, May 2014.
- Winkler, W. E. (2008), “General Discrete-data Modeling Methods for Producing Synthetic Data with Reduced Re-identification Risk that Preserve Analytic Properties,” IAB Workshop on Confidentiality and Disclosure, http://fdz.iab.de/en/FDZ_Events/SDC-Workshop.aspx, Nuremberg, Germany, November 20-21, 2008 (also http://www.census.gov/srd/papers/pdf /rrs2010-02 ).
- Winkler, W. E. (2010), “General Discrete-data Modeling Methods for Creating Synthetic Data with Reduce Re-identification Risk that Preserve Analytic Properties,” http://www.census.gov/srd/papers/pdf/rrs2010-02.pdf .
- Winkler, W.E. (2013c), Cleanup and Analysis of Sets of National Files, Federal Committee on Statistical Methodology, Proceedings of the Bi-Annual Research Conference, http://www.copafs.org/UserFiles/file/fcsm/J1_Winkler_2013FCSM.pdf., https://fcsm.sites.usa.gov/files/2014/05/J1_Winkler_2013FCSM.pdf
Review Articles
- Fienberg, Stephen, "Confidentiality and Disclosure Limitation," Encyclopedia of Social Measurement, Volume 1, 2005. A good overview article about statistical disclosure limitation, not too much math. No mention of differential privacy, of course.
Critiques
Many contemporary statistical disclosure control techniques, such as generalization and cell suppression, have been shown to be vulnerable to attack by a hypothetical data intruder. For example, Cox showed in 2009 that Complementary cell suppression typically leads to "over-protected" solutions because of the need to suppress both primary and complementary cells, and even then can lead to the compromise of sensitive data when exact intervals are reported.[1]
- ↑ Lawrence H. Cox, Vulnerability of Complementary Cell Suppression to Intruder Attack, Journal of Privacy and Confidentiality (2009) 1, Number 2, pp. 235–251 http://repository.cmu.edu/cgi/viewcontent.cgi?article=1017
