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:

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.

Elizabeth BradleyElizabeth Bradley
University of Colorado, Boulder

Bio

Elizabeth Bradley Website


Liz Bradley received the S.B., S.M., and Ph.D. degrees from the Massachusetts Institute of Technology in 1983, 1986, and 1992, respectively, including a one-year leave of absence to compete in the 1988 Olympic Games. She has been with the Department of Computer Science at the University of Colorado at Boulder since January of 1993; she also holds appointments and affiliations with a variety of engineering departments. Her current research activities focus on nonlinear dynamics and chaos, as well as scientific computation and AI. She is a member of Eta Kappa Nu, Tau Beta Pi, and Sigma Xi, as well as the recipient of a National Young Investigator award, a Packard Fellowship, and the 1999 College of Engineering teaching award.

Vasant HonavarVasant Honavar
Pennsylvania State University

Bio

Vasant Honavar Website


Vasant Honavar is a Professor and Edward Frymoyer Chair of Information Sciences and Technology and Professor of Bioinformatics and Genomics and of Neuroscience at Pennsylvania State University where he currently leads the Artificial Intelligence Research Laboratory and the Big Data Analytics and Discovery Informatics Initiative. Honavar has served as a Program Director in the Information and Intelligent Systems Division at the National Science Foundation (during 2010-2013) where he contributed to multiple programs including Information Integration and Informatics, Smart and Connected Health, and led the Big Data Science and Engineering Program. Prior to joining Pennsylvania State University, Honavar was Professor of Computer Science and of Bioinformatics and Computational Biology and Director of the Artificial Intelligence Research Laboratory (during 1990-2013), and Chair of the Bioinformatics and Computational Biology Ph.D. program (during 2003-2005) at Iowa State University. He served on the National Institutes of Health study section on Biological Data Management and Analysis during 2002-2007. Honavar’s current research and teaching interests span Artificial Intelligence, Machine Learning, Bioinformatics, Big Data Analytics, Computational Molecular Biology, Data Mining, Discovery Informatics, Information Integration, Knowledge Representation and Inference, Semantic Technologies, Health Informatics, Neuroinformatics, Social Informatics and Security Informatics. His research (documented in over 250 peer-reviewed publications) has contributed scalable approaches to learning predictive models from “big data” – including in particular, very large, distributed, semantically disparate, richly structured data (including tabular, sequence, network, relational, time series data); knowledge-based, statistical and network-based approaches to integrating information, Eliciting causal information from multiple sources of observational and experimental data; Selective sharing of knowledge across disparate knowledge bases; Representing and reasoning about preferences; Composing complex services from components; and applications in bioinformatics and computational molecular and systems biology. Honavar has graduated over 30 PhD students, many of whom are leaders in academia and industry. Honavar currently serves on the editorial boards of several journals including IEEE/ACM Transactions on Computational Biology and Bioinformatics. He has served as a general co-chair of the IEEE International Conference on Big Data (2014). Honavar earned his Ph.D. degree from the University of Wisconsin-Madison in 1990.

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.