Privacy and Confidentiality Committee Sponsors Webinar, JSM Sessions

The ASA Privacy and Confidentiality Committee, in partnership with the Federal Committee on Statistical Methodology Confidentiality and Data Access Interest Group, is sponsoring an upcoming seminar series on issues relating to statistical confidentiality and data access. 

This monthly series will explore topics such as assessing disclosure risk, commonly used disclosure avoidance techniques, governance of the disclosure review process, methods for generating and evaluating synthetic data, differential privacy, and tiered data access models. 

The seminar series will kick off on July 15 at 3:00 p.m. EDT with an introductory overview of disclosure avoidance across the federal statistical system. Visit the committee’s website for updates and information about the webinar series. To learn more, sign up for the FCSM Confidentiality and Data Access Interest Group’s mailing list by sending an email to FCSM-CDAC-subscribe-request@listserv.gsa.gov.  

Privacy and Confidentiality Sessions at JSM 

Programming for this year’s JSM includes many sessions on privacy, confidentiality, disclosure limitation, synthetic data, and differential privacy. While there are too many to include here, highlights from the program include following:  
 

Sunday, August 2 

• 4:00 – 5:50 p.m. Updates on the 2030 Census Disclosure Avoidance Research Program 

Monday, August 3 

• 8:30 – 10:20 a.m. Synthetic Data: Applications Across the Federal Statistical System   

• 2:00 – 3:50 p.m. Advancing Privacy and Usability: Applications for Data-Informed Decision-Making in Society  

Tuesday, August 4  

• 12:30 – 1:50 p.m. (Roundtable) A Community-Driven Differential Privacy Deployment Registry 

• 2:00 – 3:50 p.m. (IOL) Statistical Data Privacy for Transparent and Reproducible Research  

• 4:00 – 5:50 p.m. (Deming Lecture) Rethinking Statistical Quality in a Data-Rich World: A Comprehensive Life Cycle Framework  

Together, these sessions offer an opportunity to engage with current work on disclosure avoidance, synthetic data, privacy-preserving methods, transparency, reproducibility, and the practical governance challenges shaping the future of statistical disclosure limitation.