Strategies in Mitigating Disclosure Risk in Disaggregated Racial/Ethnic Data
Strategies in Mitigating Disclosure Risk in Disaggregated Racial/Ethnic Data
Published: 04/30/2021

The Strategies in Mitigating Disclosure Risk in Disaggregated Racial/Ethnic Data workshop reviews several commonly used statistical disclosure limitation (SDL) techniques that are used to protect sensitive government data.

In addition to this review, the presenter demonstrates these techniques in practice by presenting several case studies. Finally, the presenter shares several useful resources that the audience can refer to for further guidance on available techniques used to protect its data.

Darius Singpurwalla, Mathematical Statistician, National Science Foundation’s National Center for Science and Engineering Statistics (NCSES)

About the National Network of Health Surveys' Advancing Health Equity Through Data Disaggregation Workshop Series
Disaggregated race/ethnicity data is needed to expose gaps in health equities and inform policies and programs and close those gaps. The National Network of Health Surveys, part of the UCLA Center for Health Policy Research, offers a series of workshops designed to improve the disaggregation of race and ethnicity measures in health data sources. Our goal is to boost the number of subpopulation categories made available to key constituencies working to improve health equity. This is especially important for representing communities that are often “hidden” in large health data sets.

Darius Singpurwalla
Darius Singpurwalla
Download the presentation slides.

Topics and Timestamps

Confidentiality and Disclosure Avoidance Techniques – Darius Singpurwalla (5:45)  

Definitions of Terms (9:20)  

  • Disclosures (9:43) 
  • Direct Identifiers (11:05) 
  • Indirect Identifiers (11:30) 
  • Statistical Disclosure Limitation (11:59) 
  • Privacy vs. Confidentiality (12:57)
    • Importance of knowing the difference between the two

The Four Phases of Privacy Protections: A History of Confidentiality Protections (15:52)  

  • Phase 1: No (to limited) Privacy Protection  1790–1850 (20:50)  
  • Phase 2: Legally Enforceable Privacy Protections  1860–1920 (27:24)  
  • Phase 3: New Focus on Indirect Identifiers  1930–2000 (42:30) 
    • Microdata protections (55:31) 
    • Data Swapping (59:15)  
  • Phase 4: 21st Century Privacy Threats  2010–Present (1:02:43) 
    • Synthetic Data (1:09:05)
    • Differential Privacy (1:09:51)
      • Visualization of differential privacy (1:12:06)   

Famous Confidentiality Laws (30:08)  

  • Includes discussion about Confidential Information Processing and Statistical Efficiency Act (CIPSEA), Privacy Act of 1974 & Freedom of Information Act Exemption (Exemption 3), the Privacy Rule, Title 13, and FERPA   
    • Example of a Confidentiality Statement (34:08)