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Regression analysis with differential privacy preserving

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Version 2 2024-06-06, 03:06
Version 1 2020-02-06, 13:17
journal contribution
posted on 2024-06-06, 03:06 authored by X Fang, F Yu, G Yang, Y Qu
In the field of data mining, protecting sensitive data from being leaked is part of the focuses of current research. As a strict and provable definition of privacy model, differential privacy provides an excellent solution to the problem of privacy leakage. Numerous methods have been suggested to enforce differential privacy in various data mining tasks, such as regression analysis. However, existing solutions for regression analysis is less than satisfactory since the amount of noise added is excessive. What's worse, the adversary can launch model inversion attacks to infer sensitive information with the published regression model. Motivated by this, we propose a differential privacy budget allocation model. We optimize the regression model by adjusting the privacy budget allocation within the objective function. Extensive evaluation results show the superiority of the proposed model in terms of noise reduction, model inversion attack proof, and the trade-off between privacy protection and data utility.

History

Journal

IEEE access

Volume

7

Pagination

129353-129361

Location

Piscataway, N.J.

Open access

  • Yes

ISSN

2169-3536

eISSN

2169-3536

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Publisher

Institute of Electrical and Electronics Engineers

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