Noise Infusion as a Confidentiality Protection Measure for Graph-Based Statistics
Abowd, John A.; McKinney, Kevin L.
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.
Statistical Journal of the International Association for Official Statistics
Confidential Disclosure; Graph Theory; Noise-Infusion
Accepted version: Statistical Journal of the International Association for Official Statistics, forthcoming in 2016.
Previously Published As
Statistical Journal of the IAOS, vol. 32, no. 1, pp. 127-135, 2016