Editing Census (section)

==Privacy and data stewardship==
Although the census provides useful statistical information about a population, the availability of this information could sometimes lead to abuses, political or otherwise, by the linking of individuals' identities to anonymous census data.<ref>{{cite web |url=http://www.epic.org/privacy/census/ |title=The Census and Privacy |website=EPIC.org |access-date=2016-07-20 |archive-date=2016-06-13 |archive-url=https://web.archive.org/web/20160613025224/https://epic.org/privacy/census/ |url-status=live }}</ref> This is particularly important when individuals' census responses are made available in [[Microdata (statistics)|microdata]] form, but even aggregate-level data can result in privacy breaches when dealing with small areas and/or rare subpopulations.

For instance, when reporting data from a large city, it might be appropriate to give the average income for black males aged between 50 and 60. However, doing this for a town that only has two black males in this age group would be a breach of privacy because either of those persons, knowing his own income and the reported average, could determine the other man's income.

Typically, census data are processed to obscure such individual information. Some agencies do this by intentionally introducing small statistical errors to prevent the identification of individuals in marginal populations;<ref>{{cite web |url=http://www.abs.gov.au/websitedbs/D3110129.NSF/f61552cc746715bcca256eb000015277/2878e9589dd56b53ca257153007fe1f5!OpenDocument |title=Managing Confidentiality and Learning about SEIFA |publisher=Australian Bureau of Statistics |date=2006-04-18 |access-date=2010-11-30 |archive-date=2011-05-24 |archive-url=https://web.archive.org/web/20110524225948/http://www.abs.gov.au/websitedbs/D3110129.NSF/f61552cc746715bcca256eb000015277/2878e9589dd56b53ca257153007fe1f5!OpenDocument |url-status=live }}</ref> others swap variables for similar respondents. Whatever is done to reduce the privacy risk, new improved electronic analysis of data can threaten to reveal sensitive individual information. This is known as [[statistical disclosure control]].

Another possibility is to present survey results by means of statistical models in the form of a multivariate distribution mixture.<ref>{{cite web |url=http://www.jos.nu/Articles/abstract.asp?article=264673 |title=Statistical Model of the 2001 Czech Census for Interactive Presentation |vauthors=Grim J, Hora J, Somol P, Boček P, Pudil, P |year=2010 |work=Journal of Official Statistics, vol. 26, no. 4 |pages=673–94 |access-date=2011-01-07 |archive-date=2011-07-24 |archive-url=https://web.archive.org/web/20110724190636/http://www.jos.nu/Articles/abstract.asp?article=264673 |url-status=dead }}</ref> The statistical information in the form of [[Conditional probability distribution|conditional distributions]] ([[histogram]]s) can be derived interactively from the estimated [[Mixture distribution|mixture model]] without any further access to the original database. As the final product does not contain any protected microdata, the model-based interactive software can be distributed without any confidentiality concerns.

Another method is simply to release no data at all, except very large scale data directly to the central government. Differing release strategies of governments have led to an international project ([[IPUMS]]) to co-ordinate access to microdata and corresponding metadata. Such projects such as [[SDMX]] also promote standardising metadata, so that best use can be made of the minimal data available.