Privacy and Fairness Task Force

Chairs: Cynthia Dwork and Sampath Kannan

Cynthia DworkCynthia Dwork

Microsoft Research

Bio

Cynthia Dwork Website


Cynthia Dwork is known for her research placing privacy-preserving data analysis on a mathematically rigorous foundation, including the co-invention of differential privacy, a strong privacy guarantee frequently permitting highly accurate data analysis. She was elected as a Fellow of the American Academy of Arts and Sciences (AAAS) in 2008, as a member of the National Academy of Engineering in 2008, and as a member of the National Academy of Sciences in 2014. She received the Dijkstra Prize in 2007 for her work on consensus problems together with Nancy Lynch and Larry Stockmeyer. Dwork received her B.S.E. from Princeton University in 1979, graduating Cum Laude, and receiving the Charles Ira Young Award for Excellence in Independent Research. Dwork received her Ph.D. from Cornell University in 1983.

Sampath KannanSampath Kannan

University of Pennsylvania

Bio

Sampath Kannan Website


Sampath Kannan is the Henry Salvatori Professor and Department Chair in the Department of Computer and Information Science at the University of Pennsylvania. Sampath’s research spans several subfields in algorithms. In his work on massive data set algorithms, Sampath explores what can be computed efficiently, and what is not computable. He is also interested in program checking, a paradigm for ensuring the correctness of a program by observing its behavior at run-time, and in algorithmic problems in computational biology, particularly the problem of reconstructing the evolutionary history of a set of species from phenotypic and molecular sequence observations.

Current Members:

John Maron AbowdJohn Abowd
Census

Bio

John Abowd Website


Abowd’s current research focuses on the creation, dissemination, privacy protection, and use of matched longitudinal data on employers and employees. Abowd helped to found and continues to provide scientific leadership for the U.S. Census Bureau’s Longitudinal Employer-Household Dynamics Program, which integrates censuses, demographic surveys, economic surveys, and administrative data to produce research and public-use data.

Lorenzo AlvisiLorenzo Alvisi
University of Texas at Austin

Bio

Lorenzo Alvisi Website


Lorenzo Alvisi is a Professor of Computer Science at the University of Texas at Austin, where he co-leads the Laboratory for Advanced Systems Research (LASR). He received a Ph.D. in Computer Science from Cornell University, which he joined after earning a Laurea degree in Physics from the University of Bologna, Italy. His research interests are in the theory and practice of distributed computing, with a particular focus on dependability. He is a Fellow of the ACM, an Alfred P. Sloan Foundation Fellow, the recipient of a Humboldt Research Award from the Alexander von Humboldt Foundation and of an NSF Career Award, and was named Visiting Chair Professor by Shanghai Jiao Tong University.

Ashwin MachanavajjhalaAshwin Machanavajjhala
Duke University

Bio

Ashwin Machanavajjhala Website


Ashwin Machanavajjhala is an Assistant Professor in the Department of Computer Science, Duke University and an Associate Director at the Information Initiative@Duke (iiD). Previously, he was a Senior Research Scientist in the Knowledge Management group at Yahoo! Research. His primary research interests lie in algorithms for ensuring privacy in statistical databases and augmented reality applications. He is a recipient of the National Science Foundation Faculty Early CAREER award in 2013, and the 2008 ACM SIGMOD Jim Gray Dissertation Award Honorable Mention. Ashwin graduated with a Ph.D. from the Department of Computer Science, Cornell University and a B.Tech in Computer Science and Engineering from the Indian Institute of Technology, Madras.

Jerry ReiterJerry Reiter
Duke University

Bio

Jerry Reiter Website


Dr. Reiter participates in both applied and methodological research in statistical science. He is most interested in applications involving social science and public policy, although he enjoys working with researchers in all disciplines. His methodological research focuses mainly on statistical methods for protecting data confidentiality, for handling missing data, and for modeling complex data including methods for causal inferences. In 2015, The Atlantic published a story about Dr. Reiter’s research on methods for protecting data confidentiality. He is the Principal Investigator of the Triangle Census Research Network, which is funded by the National Science Foundation to improve the practice of data dissemination among federal statistical agencies. He is also the Deputy Director of the Information Initiative at Duke, an institute dedicated to research and applications in the analysis of large-scale (and not large-scale) data.

Towards a Privacy Research Roadmap for the Computing Community

In early 2015, the CCC commissioned members of the privacy research community to generate a short report to help guide strategic thinking in this space. The effort aimed to complement and synthesize other recent documents, including the White House BIG DATA: Seizing Opportunities, Preserving Values Report and the Report to the President on Big Data and Privacy: A Technological Perspective. In May, the CCC released the resultant community report, Towards a Privacy Research Roadmap for the Computing Community.

The editors of the paper describe a research agenda that seeks to lead the community to a state where:

  • We have a rigorous science of privacy that applies across different application domains;
  • We understand the needs, expectations, and incentives of the humans who use information systems, and can design systems that are sensitive to them;
  • Privacy technology research and privacy policy objectives are informed by and aligned with each other; and
  • We can engineer systems that enable us to enjoy both privacy and the benefits of data use to the maximum extent possible, showing that the tradeoff between the two can be much less stark than our current approaches offer

To reach this state, the editors believe that the research strategy needs to:

  • Emphasize understanding, defining, and measuring the privacy of information systems
  • Recognize and support the many stages and dimensions of privacy research
  • Enable interdisciplinary research strategies
  • Foster a technology-policy dialogue

Privacy by Design Workshops

The CCC also launched a series of four Privacy by Design workshops in 2015. The workshops are aimed at identifying a shared research vision to support the practice of privacy-by-design. They convene both practitioners with direct experience of the challenges in implementing privacy-by-design from a range of fields—software developers, privacy engineers, usability and interaction designers, chief privacy officers—and researchers from an equally broad range of disciplines.

Privacy by Design- State of Research and Practice
February 5-6, 2015

Regulators, academics and industry have called for privacy-by-design as a way to address growing privacy concerns with rapidly developing technology. The public and private sector are responding — hiring privacy engineers to join the ranks of privacy-oriented professionals, often working under the guidance of a chief privacy officer. Yet, implementing concepts of privacy through design is an open challenge and research area. There is a limited, disparate, and fragmented body of research affirmatively positioned as privacy-by-design.

Workshop Report

Privacy by Design- Privacy Enabling Design
May 7-8, 2015

This workshop covered the latest research results in user interface design, usability and human factors including studies of user behavior and recent findings in privacy displays, nudging, privacy preference modeling, to name a few. While regulators attempt to drive privacy-by-design, there is little evidence that the class of professionals who consider themselves designers are engaged in the conversation.

Workshop Report

Privacy by Design- Engineering Privacy
August 31-September 1, 2015

This workshop will survey emerging challenges in engineering privacy from applications of cryptographic protocols and privacy-preserving databases, to formal notations and programming languages in identity management, de-identification, and software specification. This survey will review known challenges, such as understanding privacy policies (e.g., privacy laws in regulated sectors like healthcare and finance; privacy promises in self-regulated sectors like Web services) in computational terms so that tools can be developed to help with their enforcement. The workshop will raise awareness of how well these results address the concepts and open problems identified in workshop #2, as well as serve to identify open research questions.

Workshop Report

Privacy by Design- Regulation as Catalyst
January 6-8, 2015

More details forthcoming.