doi:10.1109/ICHI.2014.45

Paper Title

A Quantitative Approach for Evaluating the Utility of a Differentially Private Behavioral Science Dataset

Authors

Stephanie A. Sanders

Erick Janssen

Keywords

behavioral science
medical data
privacy guarantees
statistical databases
longitudinal studies
data sharing
privacy preservation
statistical tests
data utility
data security
privacy mechanisms
quantitative evaluation
data analysis
database query
statistical integrity
research ethics
data privacy challenges
sensitive data
research facilitation
differential privacy

Article Type

Conference Article

Journal

Journal:2014 IEEE International Conference on Healthcare Informatics

Research Impact Tools

DOI

10.1109/ICHI.2014.45

Issue

| Page No : 1-8

Published On

September, 2014

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Abstract

Social scientists who collect large amounts of medical data value the privacy of their survey participants. As they follow participants through longitudinal studies, they develop unique profiles of these individuals. A growing challenge for these researchers is to maintain the privacy of their study participants, while sharing their data to facilitate research. Differential privacy is a new mechanism which promises improved privacy guarantees for statistical databases. We evaluate the utility of a differentially private dataset. Our results align with the theory of differential privacy and show when the number of records in the database is sufficiently larger than the number of cells covered by a database query, the number of statistical tests with results close to those performed on original data increases.