Data Privacy Day 2018 is on January 28. On this occasion, the ASA Committee on Privacy and Confidentiality hosts a webinar on privacy-related topics. This year, we are honored to have Dr. John M. Abowd present on privacy-loss budgets and their role in designing privacy protections, on Feb 1, 2018. Dr. Abowd is, Chief Scientist and Associate Director for Research and Methodology, U.S. Census Bureau, and Professor of Economics, Statistics, and Information Science at Cornell University.
Additional details are available at the webinar registration website at http://www.amstat.org/ASA/Education/Web-Based-Lectures.aspx#WPLB. Registration is free, but is required in order to view the webinar.
Abstract
For statistical agencies, the Big Bang event in disclosure avoidance occurred in 2003 when Irit Dinur and Kobbi Nissim, two well-known cryptographers, turned their attention to properties of safe systems for data publication from confidential sources. And the paradigm-shifting message was a very strong result showing that most of the confidentiality protection systems used by statistical agencies around the world, collectively known as statistical disclosure limitation, were not designed to defend against a database reconstruction attack. Such an attack recreates increasingly accurate record-level images of the confidential data as an agency publishes more and more accurate statistics from the same database. Why are we still talking about this theorem fifteen years later? What is required to modernize our disclosure limitation systems? The answer is recognizing that the database reconstruction theorem identified a real constraint on agency publication systems-there is only a finite amount of information in any confidential database. We can't repeal that constraint. But it doesn't help with the public-good mission of statistical agencies to publish data that are suitable for their intended uses. The hard work is incorporating the required privacy-loss budget constraint into the decision-making processes of statistical agencies. This means balancing the interests of data accuracy and privacy loss. A leading example of this process is the need for accurate redistricting data, to enforce the Voting Rights Act, and the protection of sensitive racial and ethnic information in the detailed data required for this activity. Wrestling with this tradeoff stares-down the database reconstruction theorem, and uses the formal privacy results that it inspired to specify the technologies. Specifying the decision framework for selecting a point on that technology has proven much more challenging. We still have a lot of work to do.
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Lars Vilhuber
Economist
Cornell University - Labor Dynamics Institute
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