Formal Privacy: Making an Impact at Large Organizations
With the growing amount of data collected every day, data confidentiality is increasingly at risk. Many of the traditional approaches to statistical disclosure control are no longer deemed sufficient to protect the confidentiality of the data. Formal privacy guarantees are provable privacy guarantees that typically hold regardless of assumed knowledge and attack strategy of a malicious user. The formal privacy guarantees are especially important for large producers of statistics, such as national statistical agencies or large private companies. These organizations are increasingly designing and engineering systems with improved disclosure limitation systems, with strong consideration for formal privacy. To learn more about this, the Committee on Privacy and Confidentiality organized a Joint Statistical Meeting session on “Formal Privacy - Making an Impact at Large Organizations.” The session brought together four experts from large organizations who have developed, proposed, and implemented formal privacy models or variants of differential privacy. The presentations described challenges, how they were met, and the outlook for future implementation of formal privacy. Lars Vilhuber, Cornell University and member of the Committee on Privacy and Confidentiality, organized the session. The Committee’s co-chair, Aleksandra Slavkovic, Pennsylvania State University, moderated the panel. This presentation introduces the speakers.
American Statistical Association's Committee on Privacy and Confidentiality
privacy; confidentiality; differential privacy
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