Abstract
John Abowd: Practical Privacy from the Trenches
The U.S. Census Bureau is committed to modernizing all of its disclosure limitation systems using formally private methods. For the 2020 Census of Population and Housing, the Census Bureau is testing a full publication system that is differntially private end-to-end starting with the final form of the edited confidential census enumerations--expected to be about 325 million individual records from 145 million households and 8 million group quarters. If successful, algorithms, implementation details, and all parameter settings will be public. If successful, they will be released beginning with the implementation for the 2018 End-to-End test. Unless another national statistical office has such a system ready to deploy before 2020, if successful, this will be the first formally private data publication system implemented by an official statistical agency for its full publication system from a major product. For a variety of reasons, I am very familiar with this work. I will discuss the issues that can be raised in a public forum.
Gerome Miklau: Principled Evaluation of Differentially Private Algorithms
The increasing complexity of differentially private algorithms poses a challenge for researchers evaluating new technical approaches and for practitioners adapting privacy algorithms to real-world tasks. In particular, deployment of these algorithms has been slowed by an incomplete understanding of the accuracy penalty they entail.
In this talk I will describe a set of evaluation principles designed to support the sound evaluation of privacy algorithms and I will review the conclusions of a thorough empirical study done in accordance with these principles. This empirical study is the basis of dpcomp.org, a public web-based system that allows users to interactively explore algorithm output in order to understand, both quantitatively and qualitatively, the error introduced by the algorithms and its dependence on key input parameters.
Our empirical evaluation raises a number of research problems in algorithm design and safe algorithm selection, and I will briefly mention our ongoing efforts to address them.
This talk is based on work joint with Michael Hay, Ashwin Machanavajjhala, Yan Chen, Ios Kotsogiannis, Ryan McKenna, and Dan Zhang.
Aleksandra Korolova: Challenges of Applying Differential Privacy: from the Industry Trenches