title: Data Anonymization that Leads to the Most Accurate Estimates of Statistical Characteristics: Fuzzy-Motivated Approach.
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-motivatedweightedestimates, and we explain how to optimally select the corresponding weights.
authors.affiliation: Applied Biomathematics, 100 North Country Rd., Setauket, NY 11733, USA.
authors.forename: G
authors.initials: G
authors.lastname: Xiang
authors.affiliation: Applied Biomathematics, 100 North Country Rd., Setauket, NY 11733, USA.
authors.forename: S
authors.initials: S
authors.lastname: Ferson
authors.affiliation: Applied Biomathematics, 100 North Country Rd., Setauket, NY 11733, USA.
authors.forename: L
authors.initials: L
authors.lastname: Ginzburg
authors.affiliation: University of Texas at El Paso, 500 W. University, El Paso, TX 79968, USA.
authors.forename: L
authors.initials: L
authors.lastname: Longpré
authors.affiliation: University of Texas at El Paso, 500 W. University, El Paso, TX 79968, USA.
authors.forename: E
authors.initials: E
authors.lastname: Mayorga
authors.affiliation: University of Texas at El Paso, 500 W. University, El Paso, TX 79968, USA.
authors.forename: O
authors.initials: O
authors.lastname: Kosheleva