Evaluating the risk of disclosure and utility in a synthetic dataset

Chen, K-C, Yu, C-M and Dargahi, T ORCID: https://orcid.org/0000-0002-0908-6483 2021, 'Evaluating the risk of disclosure and utility in a synthetic dataset' , Computers, Materials & Continua, 68 (1) , pp. 761-787.

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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.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Computers, Materials & Continua
Publisher: Tech Science Press
ISSN: 1546-2218
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Depositing User: T Dargahi
Date Deposited: 14 Jan 2021 08:11
Last Modified: 16 Feb 2022 06:34
URI: https://usir.salford.ac.uk/id/eprint/59346

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