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A Survey of Privacy Preserving Subgraph Matching Methods

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Artificial Intelligence Security and Privacy (AIS&P 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14509))

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Abstract

Due to the widespread of large-scale graph data and the increasing popularity of cloud computation, more and more graph processing tasks are outsourced to the cloud. Since graph data has rich information such as node information and edge information, a fundamental challenge is to minimize the overhead of subgraph matching without leakage of the sensitive information of graphs. This paper presents a survey of recent methods for privacy-preserving subgraph matching. Finally, this paper provides valuable insights and possible future directions.

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Author information

Authors and Affiliations

  1. Artificial Intelligence Research Institute, Guangzhou University, Guangzhou, 510006, China

    Xingjiang Cheng, Fuxing Zhang, Yun Peng, Xianmin Wang, Teng Huang, Duncan S. Wong & Changyu Dong

  2. Guangdong Mogulinker Technology Co. Ltd., Guangzhou, 518110, China

    Ziye Zhou

Authors
  1. Xingjiang Cheng
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  2. Fuxing Zhang
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  3. Yun Peng
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  4. Xianmin Wang
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  5. Teng Huang
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  6. Ziye Zhou
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  7. Duncan S. Wong
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  8. Changyu Dong
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Corresponding authors

Correspondence to Xianmin Wang or Teng Huang .

Editor information

Editors and Affiliations

  1. Rutgers University, Newark, NJ, USA

    Jaideep Vaidya

  2. Tampere University, Tampere, Finland

    Moncef Gabbouj

  3. Guangzhou University, Guangzhou, China

    Jin Li

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Cite this paper

Cheng, X. et al. (2024). A Survey of Privacy Preserving Subgraph Matching Methods. In: Vaidya, J., Gabbouj, M., Li, J. (eds) Artificial Intelligence Security and Privacy. AIS&P 2023. Lecture Notes in Computer Science, vol 14509. Springer, Singapore. https://doi.org/10.1007/978-981-99-9785-5_8

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  • DOI: https://doi.org/10.1007/978-981-99-9785-5_8

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-9784-8

  • Online ISBN: 978-981-99-9785-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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