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An Information-Driven Genetic Algorithm for Privacy-Preserving Data Publishing

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Web Information Systems Engineering – WISE 2022 (WISE 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13724))

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Abstract

Due to the expanding requirements for data publishing and growing concerns regarding data privacy, the privacy-preserving data publishing (PPDP) problem has received considerable attention from research communities, industries, and governments. However, it is challenging to tackle the trade-off between privacy preservation and data quality maintenance in PPDP. In this paper, an information-driven genetic algorithm (ID-GA) is designed to achieve optimal anonymization based on attribute generalization and record suppression. In ID-GA, an information-driven crossover operator is designed to efficiently exchange information between different anonymization solutions; an information-driven mutation operator is proposed to promote information release during anonymization; a two-dimension selection operator is designed to identify the qualities of different anonymization solutions. Experimental results verify the advantages of ID-GA in terms of solution accuracy and convergence speed. Besides, the impacts of all the proposed components are verified.

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Notes

  1. 1.

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Acknowledgements

This work was supported by The Major Key Project of PCL (Grant No. PCL2022A03, PCL2021A02, PCL2021A09).

Author information

Authors and Affiliations

  1. Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, 3011, Australia

    Yong-Feng Ge & Hua Wang

  2. Department of Computer Science and Information Technology, La Trobe University, Melbourne, 3083, Australia

    Jinli Cao

  3. Peng Cheng Laboratory, Shenzhen, 518055, China

    Yanchun Zhang

  4. Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006, China

    Yanchun Zhang

Authors
  1. Yong-Feng Ge
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  2. Hua Wang
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  4. Yanchun Zhang
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Corresponding author

Correspondence to Yong-Feng Ge .

Editor information

Editors and Affiliations

  1. University of Pau and Pays de l'Adour, Anglet, France

    Richard Chbeir

  2. The University of Queensland, Brisbane, QLD, Australia

    Helen Huang

  3. Sapienza Università di Roma, Rome, Italy

    Fabrizio Silvestri

  4. Open University of Cyprus, Nicosia, Cyprus

    Yannis Manolopoulos

  5. The New Cyber Research Department, Peng Cheng Laboratory, Shenzhen, China

    Yanchun Zhang

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Ge, YF., Wang, H., Cao, J., Zhang, Y. (2022). An Information-Driven Genetic Algorithm for Privacy-Preserving Data Publishing. In: Chbeir, R., Huang, H., Silvestri, F., Manolopoulos, Y., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2022. WISE 2022. Lecture Notes in Computer Science, vol 13724. Springer, Cham. https://doi.org/10.1007/978-3-031-20891-1_24

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  • DOI: https://doi.org/10.1007/978-3-031-20891-1_24

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