Digital Government:

Project Update - January 2004

• Stephen E. Fienberg and Julie McIntyre, "Data Swapping: Variations on a Theme by Dalenius and Reiss." Submitted to Privacy in Statistical Databases '2004, Barcelona, June 2004.
  
  
• S. Gomatam, A. F. Karr and A. P. Sanil (2003). "Data swapping as a decision problem." Submitted to the Journal of Official Statistics.
  
  
  ABSTRACT: Given the public's ever-increasing concerns about data confidentiality, in the near future statistical agencies be unable or unwilling, or even may not be legally allowed, to release any genuine microdata (data on individual units, such as individuals or establishments). In such a world, an alternative dissemination strategy is remote access analysis servers, to which users submit requests for output from statistical models fit using the data, but are not allowed access to the data themselves. Analysis servers, however, are not free from the risk of disclosure, especially in the face of multiple, interacting queries. We describe these risks and propose quantifiable measures of risk and data utility that can be used to specify which queries can be answered, and with what output. The risk-utility framework is illustrated for regression models.
  
  
• A. F. Karr, X. Lin, J. P. Reiter and A. P. Sanil (2004). "Secure regression on distributed databases." Submitted to the Journal of Computational and Graphical Statistics.
  
  
  ABSTRACT: We present several methods for performing linear regression on the union of distributed databases that preserve, to varying degrees, confidentiality of those databases. Such methods can be used by federal or state statistical agencies to share information from their individual databases, or to make such information available to others. Secure data integration, which provides the lowest level of protection, actually integrates the databases, but in a manner that no database owner can determine the origin of any records other than its own. Regression, associated diagnostics or any other analysis then can be performed on the integrated data. Secure multi-party computation based on shared local statistics effects computations necessary to compute least squares estimators of regression coefficients and error variances by means of analogous local computations that are combined additively using the secure summation protocol. We also provide two approaches to model diagnostics in this setting, one using shared residual statistics and the other using secure integration of synthetic residuals.
  
  
• Aleksandra B. Slavkovic and Stephen E. Fienberg, "Bounds for Cell Entries in Two-Way Tables Given Conditional Frequencies." Submitted to Privacy in Statistical Databases '2004, Barcelona, June 2004.

• George T. Duncan presented a talk entitled "Exploring the Tension between Privacy and the Social Benefits of Government Databases". Duncan was invited by the Economic and Social Research Council of Great Britain to lead a virtual conference using AccessGrid technology. With Duncan speaking from the Pittsburgh Supercomputer Center on January 14th, participants were organized to meet with him at three sites: Imperial College, London, the University of Manchester, and the University of Southhampton.
  
  
• Stephen E. Fienberg: "Statistical Disclosure Limitation: Releasing Useful Survey Data for Statistical Analysis," IMS-ASA-SRMS Joint Mini Meeting on Current Trends in Sample Surveys and Official Statistics, Calcutta, India. January 2, 2004.
  
  
• Stephen E. Fienberg: "Characterizing Probability Distributions Associated With Multi-way Contingency Tables," Department of Statistics, Tel Aviv University, Tel Aviv, Israel, January 20, 2004.
  
  
• Stephen E. Fienberg: "Characterizing Probability Distributions Associated With Multi-way Contingency Tables," Department of Statistics, Haifa University, Haifa, Israel, January 20, 2004.

• Expansion and diversification of the NISS research program. NISS research encompasses both cross-disciplinary projects and issues of national importance lying at interfaces between industry/government and academia. The Associate Director will play a leadership role in identifying problems by working with NISS affiliates and others, forming research teams, preparing proposals having strong participation from the community, and performing and managing the research.
  
  
• Management of the NISS-SAMSI relationship, from science to logistics. This will require particular attention to the role of NISS in stimulating SAMSI programs. It may also involve significant leadership responsibility for the SAMSI and NISS postdoctoral programs, including recruitment, mentoring and research activities.

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