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Webinar Details

Differential Privacy in the Real World

Claire McKay Bowen
Thursday, July 8, 2021
1:00pm Eastern Time

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‎‎About This Course:
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The collection and dissemination of data can greatly benefit society by enabling high quality ‎‎policy-relevant research projects. However, there are privacy risks inherent in sharing ‎sensitive data. Revealing too much location information places people at risk, for example by ‎empowering stalkers to more easily track people, but too little personal location ‎‎information severely hinders the effectiveness of contact tracing. These conversations ‎highlight a critical tension between personal privacy and the common good, but often ‎overlooked in these conversations ‎is how the personal privacy loss is not distributed ‎equally in society.‎

Statistical disclosure control (SDC) or limitation are methods that aim to release high-quality ‎‎data products while preserving the confidentiality of sensitive data. These techniques ‎have existed within the statistics field since the mid-twentieth century, but over the past ‎two decades the data landscape has dramatically changed. Data adversaries (or ‎intruders) can more easily reconstruct datasets and identify individuals from supposedly ‎anonymized data with the advances in modern information infrastructure and ‎computation. While traditional methods of SDC and secure data centers are still used ‎extensively, varying opinions about procedures have been developed across academia, ‎government, and industry and in different countries. A definition known as differential ‎privacy (DP) has garnered much attention, and many researchers and data maintainers ‎are moving to develop and implement differentially private methods.‎‎‎‎

‎‎‎‎However, many approaches that satisfy DP have issues, such as applying only to a particular ‎‎data type, making unrealistic assumptions about publicly available knowledge, lacking ‎applications on real-world data, and being computationally demanding or unfeasible for ‎‎the average data curator. In this talk, Urban ‎‎Institute Lead Data Scientist‎ Claire McKay Bowen‎ will introduce what DP is, survey how DP is being ‎implemented, and cover the current standing challenges in applying differentially private ‎‎methods to real-world data.‎

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About the Instructor:
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2021_Claire-green_white-headshot.jpgClaire McKay Bowen, Lead Data Scientist, is the lead data scientist for privacy and data security at the Urban ‎‎Institute. Her research focuses on assessing the quality of differentially private data synthesis ‎‎methods and science communication. In 2021, the Committee of Presidents of Statistical ‎‎Societies identified her as an emerging leader in statistics for her technical contributions and ‎‎leadership to statistics and the field of data privacy and confidentiality.‎
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