Affiliations: [a] Cornell University, Ithaca, NY, USA | [b] U.S. Census Bureau, USA
Corresponding author: John M. Abowd, 261 Ives Hall, Cornell University, Ithaca, NY 14853, USA. Tel.: +1 607 255 8024; E-mail:firstname.lastname@example.org
Abstract: We use the bipartite graph representation of
longitudinally linked employer-employee data, and the associated projections
onto the employer and employee nodes, respectively, to characterize the set
of potential statistical summaries that the trusted custodian might produce.
We consider noise infusion as the primary confidentiality protection method.
We show that a relatively straightforward extension of the dynamic
noise-infusion method used in the U.S. Census Bureau's Quarterly Workforce
Indicators can be adapted to provide the same confidentiality guarantees for
the graph-based statistics: all inputs have been modified by a minimum
percentage deviation (i.e., no actual respondent data are used) and, as the
number of entities contributing to a particular statistic increases, the
accuracy of that statistic approaches the unprotected value. Our method also
ensures that the protected statistics will be identical in all releases
based on the same inputs.
Keywords: Confidential disclosure, noise-infusion, graph theory