[OPLIN 4cast] OPLIN 4Cast #752: Can the U.S. Census stay private in a world that isn't?

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OPLIN 4Cast #752: Can the U.S. Census stay private in a world that isn't?
May 26th, 2021

[image: Magnifying glass hovering over a collage of many people] There's at
least one library product for sale that can merge your patron and
circulation data with Big Data, the exponentially expanding trove of
information about our individual daily lives, providing good insights into
building neighborhood branch collections, developing relevant programs and
services, or improving promotions. But increasingly powerful computers
combined with widely available personal information means that no one's
individual privacy and anonymity can be assumed or taken for granted.

This is a problem for the US Census, which is required *by law* to ensure
that the data it publishes can't be used to identify individual
respondents. But Census researchers, using only partial Census data and a
few commercial datasets, were able to accurately match more than a third of
the population to the confidential information they shared on Census
surveys.

In 2017, the bureau decided to implement differential privacy to protect
the anonymity of individual survey responses. Differential privacy
introduces random noise to the datasets, and while the high-level counts (a
state's population, for example) will be accurate, as you try to zoom in on
smaller groupings those counts deviate further and further from the truth.

   -
   - For The U.S. Census, Keeping Your Data Anonymous And Useful Is A
   Tricky Balance
   <https://www.npr.org/2021/05/19/993247101/for-the-u-s-census-keeping-your-data-anonymous-and-useful-is-a-tricky-balance>
   [*NPR*] "The Census Bureau has relied on the promise of confidentiality
   to get many of the country's residents to volunteer their information once
   a decade, especially among people of color, immigrants and other
   historically undercounted groups who may be unsure about how their
   responses could be used against them. But it is becoming harder for the
   bureau to uphold that pledge and continue releasing statistics from the
   census. Advances in computing and access to voter registration lists and
   commercial data sets that can be cross-referenced have made it easier to
   trace purportedly anonymized information back to an individual person."
   - Will New Privacy Changes Protect Census Data or Make Things Worse?
   <https://themarkup.org/ask-the-markup/2021/05/11/will-new-privacy-changes-protect-census-data-or-make-things-worse>
   [*The Markup*] "When Washington State officials examined an early
   demonstration set... it found 401 Census blocks where the entire population
   was over 85 years old and 3,353 where the entire population was under 14.
   An Alabama analysis of the same dataset showed 13,000 blocks where there
   were children but no adults."
   - 16 states back Alabama’s challenge to Census privacy tool
   <https://apnews.com/article/race-and-ethnicity-alabama-florida-census-2020-us-news-3407a2d05be23972741294bc05838801>
   [*AP*] "The 16 states supporting Alabama said that differential
   privacy’s use in the redistricting numbers will make the figures inaccurate
   for all states, especially at small geographic levels, and the Census
   Bureau could use other methods to protect people’s privacy."
   - What Should Librarians Know About Differential Privacy and the 2020
   Census?
   <https://www.fdlp.gov/what-should-librarians-know-about-differential-privacy-and-the-2020-census>
   [*Federal Depository Library Program*] "Librarians tend to work with
   users; they work with the general public. People come to you and ask
   questions about what data are available, whether or not that data is
   comparable to data that we are collecting in 2000 or 2010 or 1990. It is
   critical for librarians to understand what types of changes are going on to
   the census data and what that means for data users that come to you for
   support."

*From the Ohio Web Library <http://ohioweblibrary.org>:*

   - Chen, Angela. “Differential Privacy
   <https://search-ebscohost-com.proxy.oplin.org/login.aspx?direct=true&db=sch&AN=141708723&site=ehost-live>
   .” *MIT Technology Review*, vol. 123, no. 2, Mar. 2020, p. 27.
   - Weiss, Todd R. “Apple to Use Differential Privacy to Get User Insights
   Without IDs
   <https://search-ebscohost-com.proxy.oplin.org/login.aspx?direct=true&db=f5h&AN=116244442&site=ehost-live>
   .” *EWeek*, June 2016, p. 1.
   - Krieger, Nancy, et al. “Impact of Differential Privacy and Census
   Tract Data Source (Decennial Census Versus American Community Survey) for
   Monitoring Health Inequities
   <https://search-ebscohost-com.proxy.oplin.org/login.aspx?direct=true&db=pbh&AN=148105793&site=ehost-live>
   .” *American Journal of Public Health*, vol. 111, no. 2, Feb. 2021, pp.
   265–268.

------------------------------
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