Researchers at Drexel and Vanderbilt universities have proposed a new technique for permanently altering survey datasets to shield consumers’ privacy, while retaining a reasonable degree of accuracy.
Drexel's Matthew Schneider and Vanderbilt's Dawn Iacobucci analyzed a survey dataset collected in 2015 by the city of Austin, TX, and released to the public following an Open Data movement.
For a survey of the city's Asian-Americans, each respondent was asked their ethnic origin, which had 32 categories; age, which had 77 categories; zip code, which had 61 categories; and gender.
The researchers based their technique on a method used in genomic sequencing applications that conceals consumers' identity while maintaining the accuracy of insights within 5%.
Schneider said the methodology basically "shuffles" the dataset's demographic data, but only "when it maintains the correlations between important variables that are essential to analysts."
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