Evaluating the risk of disclosure and utility in a synthetic dataset

Chen, K-C; Yu, C-M; Dargahi, T

Abstract

The advancement of information technology has improved the delivery
of financial services by the introduction of Financial Technology (FinTech). To
enhance their customer satisfaction, Fintech companies leverage artificial
intelligence (AI) to collect fine-grained data about individuals, which enables them
to provide more intelligent and customized services. However, although visions
thereof promise to make our lives easier, they also raise major security and privacy
concerns for their users. Differential privacy (DP) is a popular technique for
protecting individual privacy and at the same time for releasing data for public
use. However, very few research efforts have been devoted to maintaining a
balance between the corresponding risk of data disclosure (RoD) and data utility.
In this paper, we propose data-driven approaches to differentially release private
data to evaluate the RoD. We develop algorithms to evaluate whether the
differentially private synthetic dataset offers sufficient privacy. In addition to
privacy, the utility of the synthetic dataset is an important metric for the
differential release of private data. Thus, we propose a data-driven algorithm that
uses curve fitting to measure and predict the error of the statistical result incurred
by adding random noise to the original dataset. We also present an algorithm for
choosing an appropriate privacy budget ϵ to maintain the balance between privacy
and utility. Our comprehensive experimental analysis proves both the efficiency
and estimation accuracy of the proposed algorithms.

Citation

Chen, K.-C., Yu, C.-M., & Dargahi, T. (2021). Evaluating the risk of disclosure and utility in a synthetic dataset. Computers, Materials & Continua, 68(1), 761-787. https://doi.org/10.32604/cmc.2021.014984

• Journal Article Type: Article
• Acceptance Date: Jan 13, 2021
• Publication Date: Mar 22, 2021
• Deposit Date: Jan 14, 2021
• Publicly Available Date: Jan 18, 2021
• Journal: Computers, Materials & Continua
• Print ISSN: 1546-2218
• Electronic ISSN: 1546-2226
• Publisher: Tech Science Press
• Volume: 68
• Issue: 1
• Pages: 761-787
• DOI: https://doi.org/10.32604/cmc.2021.014984
• Publisher URL: https://doi.org/10.32604/cmc.2021.014984
• Related Public URLs: https://www.techscience.com/cmc/