首页> 外文会议>International conference on data science engineering >Disclosure risk of individuals: A k-anonymity study on health care data related to Indian population
【24h】

Disclosure risk of individuals: A k-anonymity study on health care data related to Indian population

机译:个人的披露风险:与印度人口有关的卫生保健数据的k-匿名研究

获取原文

摘要

Many private organizations are reluctant to share the health related information to the researchers fearing loss of privacy of data. The non availability of data has potential negative implications for the advancement of medical science, the development of new pharmaceutical products, better diagnosis of disease and for national and micro-level health planning. So in this context, the need for the development of reliable and robust anonymization techniques for data, especially relating to health care data has become equipped. This paper attempts to illustrate the disclosure risk of individuals' health records related to Indian population and analyze the need for the development of suitable mechanisms to protect privacy of individuals. The data we have used for our evaluation purposes are made available to us by the nodal agency International Institute for Population Sciences (UPS), Mumbai. It is the data collected as part of the latest National Family Health Survey conducted in the year 2005, namely NFHS-3. Checking with k-anonymity property, the result shows that some of the individuals are at the risk of disclosure, if the actual table is linked to some other publicly available tables. This paper also attempts to illustrate the need for flexible selection of relevant attributes, especially the Quasi-Identifier (QI) attributes for the wide spread acceptance of knowledge-based systems as per the context.
机译:许多私人组织担心担心丢失数据隐私,不愿与研究人员共享与健康相关的信息。无法获得数据可能对医学科学的进步,新药品的开发,疾病的更好诊断以及国家和微观健康计划产生负面影响。因此,在这种情况下,已经满足了开发可靠且健壮的数据匿名化技术的需求,尤其是与医疗保健数据有关的匿名化技术。本文试图说明与印度人口有关的个人健康记录的披露风险,并分析了开发适当的机制来保护个人隐私的必要性。我们用于评估目的的数据由节点机构国际孟买人口研究所(UPS)提供给我们。该数据是作为2005年进行的最新全国家庭健康调查(即NFHS-3)的一部分而收集的。通过k匿名属性检查,结果表明,如果实际表链接到其他一些公共可用表,则某些个人有被披露的风险。本文还试图说明需要灵活选择相关属性,尤其是准标识符(QI)属性,以便根据上下文广泛接受基于知识的系统的情况。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号