Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Make assurance double sure: combination of two disclosure limitation methods and estimation of general regression models
AU - Nolte, Sandra
AU - Flossmann, Anton
PY - 2008/12
Y1 - 2008/12
N2 - In order to guarantee confidentiality and privacy of firm-level data, statistical offices apply various disclosure limitation techniques. However, each anonymization technique has its protection limits such that the probability of disclosing the individual information for some observations is not minimized. To overcome this problem, we propose combining two separate disclosure limitation techniques, blanking and multiplication of independent noise, in order to protect the original dataset. The proposed approach yields a decrease in the probability of reidentifying/disclosing individual information and can be applied to linear and nonlinear regression models.We show how to combine the blanking method with the multiplicative measurement error method and how to estimate the model by combining the multiplicative Simulation-Extrapolation (M-SIMEX) approach from Nolte (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=969599, 2007) on the one side with the Inverse Probability Weighting (IPW) approach going back to Horwitz and Thompson (J. Am. Stat. Assoc. 47:663–685, 1952) and on the other side with matching methods, as an alternative to IPW, like the semiparametric M-Estimator proposed by Flossmann (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=917326, 2007). Based on Monte Carlo simulations, we show that multiplicative measurement error combined with blanking as a masking procedure does not necessarily lead to a severe reduction in the estimation quality, provided that its effects on the data generating process are known.
AB - In order to guarantee confidentiality and privacy of firm-level data, statistical offices apply various disclosure limitation techniques. However, each anonymization technique has its protection limits such that the probability of disclosing the individual information for some observations is not minimized. To overcome this problem, we propose combining two separate disclosure limitation techniques, blanking and multiplication of independent noise, in order to protect the original dataset. The proposed approach yields a decrease in the probability of reidentifying/disclosing individual information and can be applied to linear and nonlinear regression models.We show how to combine the blanking method with the multiplicative measurement error method and how to estimate the model by combining the multiplicative Simulation-Extrapolation (M-SIMEX) approach from Nolte (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=969599, 2007) on the one side with the Inverse Probability Weighting (IPW) approach going back to Horwitz and Thompson (J. Am. Stat. Assoc. 47:663–685, 1952) and on the other side with matching methods, as an alternative to IPW, like the semiparametric M-Estimator proposed by Flossmann (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=917326, 2007). Based on Monte Carlo simulations, we show that multiplicative measurement error combined with blanking as a masking procedure does not necessarily lead to a severe reduction in the estimation quality, provided that its effects on the data generating process are known.
KW - disclosure limitation technique
KW - multiplicative measurement error
KW - blanking
KW - simulation-extrapolation
KW - inverse probability weighting
KW - matching
U2 - 10.1007/s10182-008-0094-x
DO - 10.1007/s10182-008-0094-x
M3 - Journal article
VL - 92
SP - 405
EP - 422
JO - AStA Advances in Statistical Analysis
JF - AStA Advances in Statistical Analysis
SN - 1863-8171
IS - 4
ER -