The NCRN-Cornell node is a part of the NSF-Census Research Network, the first nodes of which were created and funded in 2011.

For more information, see http://www.ncrn.cornell.edu

Recent Submissions

  • Proceedings from the 2016 NSF–Sloan Workshop on Practical Privacy 

    Vilhuber, Lars; Schmutte, Ian; Abowd, John M. (2017-01-22)
    On October 14, 2016, we hosted a workshop that brought together economists, survey statisticians, and computer scientists with expertise in the field of privacy preserving methods: Census Bureau staff working on implementing ...
  • CED 2 AR: The Comprehensive Extensible Data Documentation and Access Repository 

    Lagoze, Carl; Vilhuber, Lars; Williams, Jeremy; Perry, Benjamin; Block, William C. (Digital Libraries (JCDL), 2014 IEEE/ACM Joint Conference on, 2014-12-04)
    We describe the design, implementation, and deployment of the Comprehensive Extensible Data Documentation and Access Repository (CED 2 AR). This is a metadata repository system that allows researchers to search, browse, ...
  • How Will Statistical Agencies Operate When All Data Are Private? 

    Abowd, John M. (Journal of Privacy and Confidentiality, 2016-09-06)
    The dual problems of respecting citizen privacy and protecting the confidentiality of their data have become hopelessly conflated in the “Big Data” era. There are orders of magnitude more data outside an agency’s firewall ...
  • Synthetic Establishment Microdata Around the World 

    Vilhuber, Lars; Abowd, John A.; Reiter, Jerome P. (Statistical Journal of the International Association for Official Statistics, 2016)
    In contrast to the many public-use microdata samples available for individual and household data from many statistical agencies around the world, there are virtually no establishment or firm microdata available. In large ...
  • Using Partially Synthetic Microdata to Protect Sensitive Cells in Business Statistics 

    Vilhuber, Lars; Miranda, Javier (Statistical Journal of the International Association for Official Statistics, 2016)
    We describe and analyze a method that blends records from both observed and synthetic microdata into public-use tabulations on establishment statistics. The resulting tables use synthetic data only in potentially sensitive ...

View more

Statistics

RSS Feeds