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Effects of Data Anonymization by Cell Suppression on Descriptive Statistics and Predictive Modeling Performance

Affiliations of the authors: Decision Systems Group, Brigham and Women’s Hospital, Harvard Medical School, Division of Health Sciences and Technology, Massachusetts Institute of Technology, Boston, Massachusetts (LO-M, SV); and Department of Software Engineering for Medicine, Polytechnic University of Upper Austria, Hagenberg, Austria (SD). e-mail: <machado{at}dsg.harvard.edu>.

Abstract

Protecting individual data in disclosed databases is essential. Data anonymization strategies can produce table ambiguation by suppression of selected cells. Using table ambiguation, different degrees of anonymization can be achieved, depending on the number of individuals that a particular case must become indistinguishable from. This number defines the level of anonymization. Anonymization by cell suppression does not necessarily prevent inferences from being made from the disclosed data. Preventing inferences may be important to preserve confidentiality. We show that anonymized data sets can preserve descriptive characteristics of the data, but might also be used for making inferences on particular individuals, which is a feature that may not be desirable. The degradation of predictive performance is directly proportional to the degree of anonymity. As an example, we report the effect of anonymization on the predictive performance of a model constructed to estimate the probability of disease given clinical findings.