Multi-level privacy preserving k-anonymity

Jui Hung Weng, Po Wen Chi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


k-anonymity is a well-known definition of privacy, which guarantees that any person in the released dataset cannot be distinguished from at least k-1 other individuals. In the protection model, the records are anonymized through generalization or suppression with a fixed value of k. Accordingly, each record has the same level of anonymity in the published dataset. However, different people or items usually have inconsistent privacy requirements. Some records need extra protection while others require a relatively low level of privacy constraint. In this paper, we propose Multi-Level Privacy Preserving K-Anonymity, an advanced protection model based on k-anonymity, which divides records into different groups and requires each group to satisfy its respective privacy requirement. Moreover, we present a practical algorithm using clustering techniques to ensure the property. The evaluation on a real-world dataset confirms that the proposed method has the advantages of offering more flexibility in setting privacy parameters and providing higher data utility than traditional k-anonymity.

Original languageEnglish
Title of host publicationProceedings - 2021 16th Asia Joint Conference on Information Security, AsiaJCIS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Electronic)9781665417884
Publication statusPublished - 2021 Aug
Event16th Asia Joint Conference on Information Security, AsiaJCIS 2021 - Seoul, Korea, Republic of
Duration: 2021 Aug 192021 Aug 20

Publication series

NameProceedings - 2021 16th Asia Joint Conference on Information Security, AsiaJCIS 2021


Conference16th Asia Joint Conference on Information Security, AsiaJCIS 2021
Country/TerritoryKorea, Republic of


  • Anonymization
  • Data privacy
  • k-anonymity

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality


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