Data Anonymization that Leads to the Most Accurate Estimates of Statistical Characteristics: Fuzzy-Motivated Approach

Authors

G. Xiang, Applied BiomathematicsFollow
S. Ferson, Applied Biomathematics
L. Ginzburg, Applied Biomathematics
L. Longpre, The University of Texas at El PasoFollow
E. Mayorga, The University of Texas at El Paso
O. Kosheleva, The University of Texas at El PasoFollow

Publication Date

3-2013

Comments

Technical Report: UTEP-CS-13-12a

To appear in Proceedings of the joint World Congress of the International Fuzzy Systems Association and Annual Conference of the North American Fuzzy Information Processing Society IFSA/NAFIPS'2013, Edmonton, Canada, June 24-28, 2013.

Abstract

To preserve privacy, the original data points (with exact values) are replaced by boxes containing each (inaccessible) data point. This privacy-motivated uncertainty leads to uncertainty in the statistical characteristics computed based on this data. In a previous paper, we described how to minimize this uncertainty under the assumption that we use the same standard statistical estimates for the desired characteristics. In this paper, we show that we can further decrease the resulting uncertainty if we allow fuzzy-motivated weighted estimates, and we explain how to optimally select the corresponding weights.