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文章基本信息

  • 标题:Evaluating the impact of k-anonymization on the inference of interaction networks
  • 本地全文:下载
  • 作者:Pedro Rijo ; Alexandre P. Francisco ; Mário J. Silva
  • 期刊名称:Transactions on Data Privacy
  • 印刷版ISSN:1888-5063
  • 电子版ISSN:2013-1631
  • 出版年度:2016
  • 卷号:9
  • 期号:1
  • 页码:49-72
  • 出版社:IIIA-CSIC
  • 摘要:

    We address the publication of a large academic information dataset while ensuring privacy. We evaluate anonymization techniques achieving the intended protection, while retaining the utility of the anonymized data. The published data can help to infer behaviors and study interaction patterns in an academic population. These could subsequently be used to improve the planning of campus life, such as defining cafeteria opening hours or assessing student performance. Moreover, the nature of academic data is such that many implicit social interaction networks can be derived from available datasets, either anonymized or not, raising the need for researching how anonymity can be assessed in this setting. Hence we quantify the impact of anonymization techniques over data utility and the impact of anonymization on behavioural patterns analysis.

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