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A Privacy-Preserving Evolutionary Computation Framework for Feature Selection

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Web Information Systems Engineering – WISE 2023 (WISE 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14306))

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Abstract

Feature selection is a crucial process in data science that involves selecting the most effective subset of features. Evolutionary computation (EC) is one of the most commonly-used feature selection techniques and has demonstrated good performance, which can help find the suitable feature subset based on training data and fitness information. However, in real-world scenarios, the exact fitness information and privacy-protected data cannot be directly accessed due to privacy and security issues, which leads to a great optimization challenge. To solve such privacy-preserving feature selection problems efficiently, this paper proposes a novel EC-based feature selection framework that balances data privacy and optimization efficiency, together with three contributions. First, based on the rank-based cryptographic function that returns the rank of solutions rather than the exact fitness information, this paper proposes a new fitness function to guide the EC algorithm to approach the global optimum without knowing the exact fitness information and the dataset, thereby preserving data privacy. Second, by integrating the proposed method and EC algorithms, this paper develops a new differential evolution and particle swarm optimization algorithms for efficient feature selection. Finally, experiments are conducted on public datasets, which demonstrate that the proposed method can maintain feature selection efficiency while preserving data privacy.

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Acknowledgment

This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 62176094, and in part by the National Research Foundation of Korea under Grant NRF-2022H1D3A2A01093478 and Grant NRF-2020R1C1C1013806.

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Authors and Affiliations

  1. Nankai University, Tianjin, China

    Bing Sun, Jian-Yu Li, Xiao-Fang Liu & Jun Zhang

  2. Hanyang University, Ansan, South Korea

    Jun Zhang

  3. Nanjing University of Information Science and Technology, Nanjing, China

    Qiang Yang

  4. South China University of Technology, Guangzhou, China

    Zhi-Hui Zhan

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  1. Bing Sun
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  2. Jian-Yu Li
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  3. Xiao-Fang Liu
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  4. Qiang Yang
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  5. Zhi-Hui Zhan
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  6. Jun Zhang
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Corresponding authors

Correspondence to Jian-Yu Li or Zhi-Hui Zhan .

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Editors and Affiliations

  1. Renmin University of China, Beijing, China

    Feng Zhang

  2. Victoria University, Footscray, VIC, Australia

    Hua Wang

  3. Qatar University, Doha, Qatar

    Mahmoud Barhamgi

  4. Swinburne University of Technology, Hawthorn, Australia

    Lu Chen

  5. Swinburne University of Technology, Hawthorn, Australia

    Rui Zhou

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Sun, B., Li, JY., Liu, XF., Yang, Q., Zhan, ZH., Zhang, J. (2023). A Privacy-Preserving Evolutionary Computation Framework for Feature Selection. In: Zhang, F., Wang, H., Barhamgi, M., Chen, L., Zhou, R. (eds) Web Information Systems Engineering – WISE 2023. WISE 2023. Lecture Notes in Computer Science, vol 14306. Springer, Singapore. https://doi.org/10.1007/978-981-99-7254-8_20

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