Communicating Statistical Disclosure Avoidance Measures to Data Users

• Activity Number: 275 - Communicating Statistical Disclosure Avoidance Measures to Data Users
• Type: Topic Contributed
• Date/Time: Tuesday, August 9, 2022 : 10:30 AM to 12:20 PM
• Sponsor: Government Statistics Section
• Author(s): Amy O'Hara* and Danah Boyd* and Michael B Hawes* and Erica Groshen*
• Companies: Georgetown University and Microsoft Research and US Census Bureau and Cornell University--ILR School
• Keywords: privacy; confidentiality; surveys; official statistics; synthetic data; differential privacy

Abstract

This panel session will discuss best practices for communicating information about disclosure avoidance methodologies and associated impacts on analyses of data. The importance and timeliness of this issue has become evident by public response to privacy modernization at the Census Bureau. While the formal privacy methods employed for the 2020 Decennial Census are relatively new, synthetic data methods, proposed for future ACS public-use microdata, have become increasingly popular and viable over the last decade, yet there continues to be suspicion that such data are 'fake' and not useful. We can do better to educate users on how the data are changing, what this means for their work, and to better understand the value and advantages of privacy modernization.