School of Interdisciplinary Mathematical Sciences, Meiji University, 4-21-1 Nakano, Tokyo, 164-8525, Japan.
Strategic Coordination of Research and Intellectual Properties, Meiji University, 4-21-1 Nakano, Tokyo, 164-8525, Japan.
J Med Syst. 2018 Apr 4;42(5):90. doi: 10.1007/s10916-018-0930-9.
Privacy preserving data mining for medical information is an important issue to guarantee confidentiality of integrated multiple data sets. In this paper, we propose a secured scheme to estimate related risk of cancers accurately and effectively in a privacy-preserving way. We study models to configure the appropriate set of attributes to reduce risk of identity of an individual from being determined. We examine the proposed privacy preserving protocol for encrypted hypothesis test, using actual cohort data supplied by National Cancer Center.
医学信息的隐私保护数据挖掘对于保证集成多数据集的机密性是一个重要问题。在本文中，我们提出了一种安全方案，以隐私保护的方式准确有效地估计癌症的相关风险。我们研究了配置适当属性集的模型，以降低个体身份被确定的风险。我们使用国家癌症中心提供的实际队列数据，检验了用于加密假设检验的隐私保护协议。
