Dr. Claire McKay Bowen is the Lead Data Scientist for Privacy and Data Security at the Urban Institute. Her research focuses on developing and assessing the quality of di erentially private data synthesis methods and science communication. She holds a BS in mathematics and physics from Idaho State University and an MS and PhD in statistics from the University of Notre Dame. After completing her PhD, she worked at Los Alamos National Laboratory, where she investigated cosmic ray effects on supercomputers.In 2021, the Committee of Presidents of Statistical Societies identified her as an emerging leader in statistics for her “contributions to the development and broad dissemination of Statistics and Data Science methods and concepts, particularly in the emerging field of Data Privacy, and for leadership of technical initiatives, professional development activities, and educational programs.”

"Staffers and non-technical policy/think-tank researchers need this book. Also data users, like state demographers or public health officials, who need to understand how their data are affected by privacy preserving approaches, but who do not need to know the technical details." -Amy O’Hara, Georgetown University"I worked in the Federal Statistical system for several years on the analysis and dissemination of household survey data, preparation of public uses files, and the submission of requests to the Disclosure Review Board. Even with my experience, I found the book fascinating, comprehensive, and valuable for developing a foundation to understand traditional and emerging data privacy issues." -Stephanie Shipp, Biocomplexity Institute, University of Virginia"The author introduces traditional data privacy techniques, such as data suppression, top-and bottom-coding and categorical thresholding, as well as modern approaches, most notably differential privacy, at a high, non-technical level. This is done in a clear and accessible way, making the book a good resource for the layperson or as a first glimpse into the complex world of data privacy. Readers with technical backgrounds may still find some of the chapters quite enlightening. I especially appreciated the discussion of issues surrounding the process of releasing personal data in the real world. ... In conclusion, Claire McKay Bowen has written a valuable introduction to the increasingly important area of data privacy."-Stefan Stein, in Journal of the Royal Statistical Society Series A (Statistics in Society), April 2022

• 1. Why Is Data Privacy Important? 2. How Did Data Privacy Change Over Time? 3. How Do Data Privacy Methods Expand Access to Data? 4. How Do Data Privacy Methods Avoid Invalidating Results? 5. What Makes Datasets Difficult for Data Privacy? 6. What Data Privacy Laws Exist? 7. What Is the Future of Data Privacy?

Dr. Claire McKay Bowen is the Lead Data Scientist for Privacy and Data Security at the Urban Institute. Her research focuses on developing and assessing the quality of di erentially private data synthesis methods and science communication. She holds a BS in mathematics and physics from Idaho State University and an MS and PhD in statistics from the University of Notre Dame. After completing her PhD, she worked at Los Alamos National Laboratory, where she investigated cosmic ray effects on supercomputers.In 2021, the Committee of Presidents of Statistical Societies identified her as an emerging leader in statistics for her “contributions to the development and broad dissemination of Statistics and Data Science methods and concepts, particularly in the emerging field of Data Privacy, and for leadership of technical initiatives, professional development activities, and educational programs.”

"Staffers and non-technical policy/think-tank researchers need this book. Also data users, like state demographers or public health officials, who need to understand how their data are affected by privacy preserving approaches, but who do not need to know the technical details." -Amy O’Hara, Georgetown University"I worked in the Federal Statistical system for several years on the analysis and dissemination of household survey data, preparation of public uses files, and the submission of requests to the Disclosure Review Board. Even with my experience, I found the book fascinating, comprehensive, and valuable for developing a foundation to understand traditional and emerging data privacy issues." -Stephanie Shipp, Biocomplexity Institute, University of Virginia"The author introduces traditional data privacy techniques, such as data suppression, top-and bottom-coding and categorical thresholding, as well as modern approaches, most notably differential privacy, at a high, non-technical level. This is done in a clear and accessible way, making the book a good resource for the layperson or as a first glimpse into the complex world of data privacy. Readers with technical backgrounds may still find some of the chapters quite enlightening. I especially appreciated the discussion of issues surrounding the process of releasing personal data in the real world. ... In conclusion, Claire McKay Bowen has written a valuable introduction to the increasingly important area of data privacy."-Stefan Stein, in Journal of the Royal Statistical Society Series A (Statistics in Society), April 2022

• 1. Why Is Data Privacy Important? 2. How Did Data Privacy Change Over Time? 3. How Do Data Privacy Methods Expand Access to Data? 4. How Do Data Privacy Methods Avoid Invalidating Results? 5. What Makes Datasets Difficult for Data Privacy? 6. What Data Privacy Laws Exist? 7. What Is the Future of Data Privacy?

Dr. Claire McKay Bowen is the Lead Data Scientist for Privacy and Data Security at the Urban Institute. Her research focuses on developing and assessing the quality of di erentially private data synthesis methods and science communication. She holds a BS in mathematics and physics from Idaho State University and an MS and PhD in statistics from the University of Notre Dame. After completing her PhD, she worked at Los Alamos National Laboratory, where she investigated cosmic ray effects on supercomputers.In 2021, the Committee of Presidents of Statistical Societies identified her as an emerging leader in statistics for her “contributions to the development and broad dissemination of Statistics and Data Science methods and concepts, particularly in the emerging field of Data Privacy, and for leadership of technical initiatives, professional development activities, and educational programs.”

"Staffers and non-technical policy/think-tank researchers need this book. Also data users, like state demographers or public health officials, who need to understand how their data are affected by privacy preserving approaches, but who do not need to know the technical details." -Amy O’Hara, Georgetown University"I worked in the Federal Statistical system for several years on the analysis and dissemination of household survey data, preparation of public uses files, and the submission of requests to the Disclosure Review Board. Even with my experience, I found the book fascinating, comprehensive, and valuable for developing a foundation to understand traditional and emerging data privacy issues." -Stephanie Shipp, Biocomplexity Institute, University of Virginia"The author introduces traditional data privacy techniques, such as data suppression, top-and bottom-coding and categorical thresholding, as well as modern approaches, most notably differential privacy, at a high, non-technical level. This is done in a clear and accessible way, making the book a good resource for the layperson or as a first glimpse into the complex world of data privacy. Readers with technical backgrounds may still find some of the chapters quite enlightening. I especially appreciated the discussion of issues surrounding the process of releasing personal data in the real world. ... In conclusion, Claire McKay Bowen has written a valuable introduction to the increasingly important area of data privacy."-Stefan Stein, in Journal of the Royal Statistical Society Series A (Statistics in Society), April 2022

• 1. Why Is Data Privacy Important? 2. How Did Data Privacy Change Over Time? 3. How Do Data Privacy Methods Expand Access to Data? 4. How Do Data Privacy Methods Avoid Invalidating Results? 5. What Makes Datasets Difficult for Data Privacy? 6. What Data Privacy Laws Exist? 7. What Is the Future of Data Privacy?