Multi-level privacy preserving k-anonymity

Jui Hung Weng, Po Wen Chi

研究成果: 書貢獻/報告類型會議論文篇章

摘要

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.

原文英語
主出版物標題Proceedings - 2021 16th Asia Joint Conference on Information Security, AsiaJCIS 2021
發行者Institute of Electrical and Electronics Engineers Inc.
頁面61-67
頁數7
ISBN(電子)9781665417884
DOIs
出版狀態已發佈 - 2021 8月
事件16th Asia Joint Conference on Information Security, AsiaJCIS 2021 - Seoul, 大韓民國
持續時間: 2021 8月 192021 8月 20

出版系列

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

會議

會議16th Asia Joint Conference on Information Security, AsiaJCIS 2021
國家/地區大韓民國
城市Seoul
期間2021/08/192021/08/20

ASJC Scopus subject areas

  • 電腦網路與通信
  • 資訊系統與管理
  • 安全、風險、可靠性和品質

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