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.
We acknowledge financial support from the U.S. Census Bureau and the National Science Foundation Grants SES-9978093 and SES-0427889 to Cornell University (Cornell Institute for Social and Economic Research), the National Institute on Aging Grant R01 AG018854-01, and the Alfred P. Sloan Foundation for LEHD infrastructure support. Abowd acknowledges additional funding through NSF Grants SES- 0922005, SES-1042181, TC-1012593 and SES-1131848.
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