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Research on Privacy Protection Methods for Data Mining

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Big Data and Security (ICBDS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1796))

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Abstract

With the applications of big data and cloud computing technologies in industries, data mining technologies have been developing rapidly in these years. However, privacy issues have been attracting attentions for users and researchers since the laws and regulations of protecting personal information are issued. How to appropriately apply data mining technologies while meeting the privacy protection requirements become an important problem to address. In this paper, the privacy preserving data mining technologies are studied including K-means, Support Vector Machine, decision tree and association rule mining. In addition to their principles, the corresponding privacy protection methods for them are discussed. Furthermore, the commonly used privacy protection methods are studied including restricted release, searchable symmetric encryption, homomorphic encryption and digital envelope. Finally, the suggestions are given that the data processing algorithms need to be improved to obtain the better balance between data mining efficiency and privacy protection, and the system could be designed to provide privacy protection measures to meet personalized demands. The studies in this paper are expected to provide technical ideas to various service providers such as personal recommendation to implement privacy protection strategies.

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Authors and Affiliations

  1. State Grid Fujian Electric Power Research Institute, Fuzhou, 350007, China

    Jindong He, Shanshan Lei & Dan Wu

  2. State Grid Fujian Electric Power Co., Ltd., 350001, Fuzhou, China

    Rongyan Cai

Authors
  1. Jindong He
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  2. Rongyan Cai
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  3. Shanshan Lei
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  4. Dan Wu
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Corresponding author

Correspondence to Jindong He .

Editor information

Editors and Affiliations

  1. Nanjing Institute of Technology, Nanjing, China

    Yuan Tian

  2. Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China

    Tinghuai Ma

  3. Chinese Academy of Sciences, Shenzhen, China

    Qingshan Jiang

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

    Qi Liu

  5. King Saud University, Riyadh, Saudi Arabia

    Muhammad Khurram Khan

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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He, J., Cai, R., Lei, S., Wu, D. (2023). Research on Privacy Protection Methods for Data Mining. In: Tian, Y., Ma, T., Jiang, Q., Liu, Q., Khan, M.K. (eds) Big Data and Security. ICBDS 2022. Communications in Computer and Information Science, vol 1796. Springer, Singapore. https://doi.org/10.1007/978-981-99-3300-6_44

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  • DOI: https://doi.org/10.1007/978-981-99-3300-6_44

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-3299-3

  • Online ISBN: 978-981-99-3300-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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