Affiliations: NORC at the University of Chicago, Bethesda, MD,
USA | Bureau of Labor Statistics, 2 Massachusetts Avenue, NE
Washington, DC, USA
Note: [] Corresponding author: Michael Yang, NORC at the University of
Chicago, 4350 East-West Highway, Bethesda, MD, USA. E-mail:
Yang-Michael@norc.org
Abstract: In recent years, the random noise method has been gaining wider use
in National Statistical Institutes (NSIs) to protect respondent data from
disclosure. The random noise method takes a micro-data approach to disclosure
limitation: a multiplier (noise factor) is applied to each unit prior to
tabulation – thus, guaranteeing that different tabulations, from the lowest to
the highest level, are consistent. In this paper we evaluate two different
random noise models in an applied context. Our analysis suggests that both the
Basic Noise Model (BNM) and the Alternate Noise Model (ANM) are unsatisfactory
for protecting smaller units. To overcome this difficulty, we developed a
(third) Mixed Perturbation Model (MPM) that combines the use of multiplicative
noise to protect large units with the use of synthetic models to protect the
smaller units. To accomplish this, we constructed a hybrid model (first
logistic and then linear) to generate the synthetic data. Results indicate that
the mixed approach performs better than either of the other two models, both in
terms of reliability and disclosure limitation, although it too has weaknesses.
Hence, areas for future study remain that our research suggests might be
tackled next.
