Dynamic-Differential Privacy based on Feature Selection with Improved Usability and Security

Sun-Jin Lee, Hye-Yeon Shim, Jung-Hwa Rye, Il-Gu Lee

2025

Abstract

With the advent of the digital transformation era, the introduction of machine learning (ML) in all industries has accelerated. ML is highly utilized because it can provide various services, such as prediction and classification. However, because the data used in the learning process contain personal information, innumerable people could be harmed if the data are leaked. Differential privacy (DP) techniques have been studied to improve data security. They are improved by adding noise from the data. However, owing to the reduced classification performance of legitimate users, they are difficult to apply in areas that require accurate prediction. This study proposes the dynamic DP based on feature selection (D-DPFS) model. D-DPFS can improve usability and security by applying DP only to privacy-related features. Experiment results indicate that D-DPFS increases the prediction accuracy to 96.37% from a usability perspective. Additionally, for users who have predefined data to prevent information leakage, security was improved by adjusting the number of features to which DP was applied according to the number of privacy features.

Paper Citation

in Harvard Style

Lee S., Shim H., Rye J. and Lee I. (2025). Dynamic-Differential Privacy based on Feature Selection with Improved Usability and Security. In Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 2: ICISSP; ISBN 978-989-758-735-1, SciTePress, pages 141-149. DOI: 10.5220/0013117100003899

in Bibtex Style

@conference{icissp25,
author={Sun-Jin Lee and Hye-Yeon Shim and Jung-Hwa Rye and Il-Gu Lee},
title={Dynamic-Differential Privacy based on Feature Selection with Improved Usability and Security},
booktitle={Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 2: ICISSP},
year={2025},
pages={141-149},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013117100003899},
isbn={978-989-758-735-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 2: ICISSP
TI - Dynamic-Differential Privacy based on Feature Selection with Improved Usability and Security
SN - 978-989-758-735-1
AU - Lee S.
AU - Shim H.
AU - Rye J.
AU - Lee I.
PY - 2025
SP - 141
EP - 149
DO - 10.5220/0013117100003899
PB - SciTePress