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Lee, Junghye
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  • Data Mining in Healthcare, Chemometrics, Machine Learning, Probabilistic Graphical Models

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Secure and Differentially Private Logistic Regression for Horizontally Distributed Data

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Title
Secure and Differentially Private Logistic Regression for Horizontally Distributed Data
Author
Kim, MiranLee, JunghyeOhno-Machado, LucilaJiang, Xiaoqian
Issue Date
2020-01
Publisher
Institute of Electrical and Electronics Engineers
Citation
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, v.15, pp.695 - 710
Abstract
Scientific collaborations benefit from sharing information and data from distributed sources, but protecting privacy is a major concern. Researchers, funders, and the public in general are getting increasingly worried about the potential leakage of private data. Advanced security methods have been developed to protect the storage and computation of sensitive data in a distributed setting. However, they do not protect against information leakage from the outcomes of data analyses. To address this aspect, studies on differential privacy (a state-ofthe-art privacy protection framework) demonstrated encouraging results, but most of them do not apply to distributed scenarios. Combining security and privacy methodologies is a natural way to tackle the problem, but naive solutions may lead to poor analytical performance. In this article, we introduce a novel strategy that combines differential privacy methods and homomorphic encryption techniques to achieve the best of both worlds. Using logistic regression (a popular model in biomedicine), we demonstrated the practicability of building secure and privacypreserving models with high efficiency (less than 3 minutes) and good accuracy (< 1 % of difference in the area under the receiver operating characteristic curve (AUC) against the global model) using a few real-world datasets.
URI
https://scholarworks.unist.ac.kr/handle/201301/26857
URL
https://ieeexplore.ieee.org/document/8747377
DOI
10.1109/tifs.2019.2925496
ISSN
1556-6013
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SME_Journal Papers
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