DIGITAL GOVERNMENT PROJECT
Progress Report: April 24, 2000
The Research Team
Alan Karr, Ashish Sanil, Jaeyong Lee [, James
Adrian Dobro, George Duncan, Stephen
Bonnie Parrish, Karen Litwin, Syam Sun-
a Web-based query system that
1. Is dynamic and history-dependent
2. Dispenses statistical analyses rather than
3. Uses statistical technology to preserve con-
the system on “live” Federal agency
how the system is used and performs
disclosure risk models and risk reduction
strategies at realistic
scales, using the systemas testbed
Summary of Progress to Date
• Algorithms for geographic (or other) aggregation (Sanil,
• Statistical implications of aggregation (Lee, Sanil, Karr)• Prototype table server design (Karr, Sanil, Hilden–Minton)• QHDB schema for table server (Sanil, Karr, Hilden–Minton)• NASS prototype under construction (Karr, Lee, Sanil,
• Scalability of methods to compute bounds (Fienberg,
• Bayesian framework for confidentiality protection (Dun-
• Confidentiality Reading Group, involving NISS, RTI, other
• Interactions with other DG projects (Columbia, UNC)
Table Server Prototype
Sample Census data set with
• 8 (after trimming) categorical variables: Age,
Education, Employer type, Marital status,
Sub-table of full 8-way table
Requested sub-table (FTP, character dis-
play, visualization) or statement that it cannot
≡ Movement of Frontier
• Predictive capability for sensitive variable
• Accuracy of IPF reconstruction of full table
• [Accuracy of LP bounds on cell entries]
• Visualization as a means of risk reduction
• Visual interfaces incorporating association
Formal results on bounds for tables and their rela-
tionship to log-linear model and graphical structures.
New theorems for the "decomposable case" and ex-tensions that reduce the bounding problem to smallerdimensional components.
With Duncan, exploration of formal structures requiredto weight the tradeoff between disclosure risk and so-cietal gains from data release, using a formal Bayesianinformation theoretic approach.
Scaling up the results so that they are
computationally feasible for actual government sur-vey settings.
Papers (PNAS); code to be incorporated in table
Initial steps toward formal Bayesian decision–the-
oretic framework for confidentiality protection throughdisclosure limitation. The framework explicitly incorpo-rates disclosure risk and data utility. It also permits thecomparison of disclosure limitation through matrix mask-ing and generation of synthetic data.
With Fienberg, exploration of formal structures requiredto weight the tradeoff between disclosure risk and so-cietal gains from data release, using a formal Bayesianinformation theoretic approach.
Formally analyze the impact on dis-
closure risk and data utility of data swapping. Bet-ter understand synthetic data as a disclosure limitationtool. Develop associated procedures for disclosure riskestimation and disclosure limitation that scale.
New algorithms. Review paper on confidential-
ity and disclosure limitation, to be published in the In-ternational Encyclopedia of the Social and BehavioralSciences (Duncan).
The Next Six Months
• Complete NASS prototype; write associated
• Functional table server prototype with dynamic
risk estimation and visualizations. Major scal-
ability questions will remain
• Initial concepts of query, risk, response for re-
• [Initial consideration of longitudinal data]
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