College of Mathematics & Computer Science, Shanxi Normal University, Linfen, 041000, China.
School of Information Science and Engineering, Linyi University, Linyi, 276000, China.
J Med Syst. 2018 Feb 14;42(3):56. doi: 10.1007/s10916-018-0896-7.
The widely use of IoT technologies in healthcare services has pushed forward medical intelligence level of services. However, it also brings potential privacy threat to the data collection. In healthcare services system, health and medical data that contains privacy information are often transmitted among networks, and such privacy information should be protected. Therefore, there is a need for privacy-preserving data collection (PPDC) scheme to protect clients (patients) data. We adopt (a,k)-anonymity model as privacy pretection scheme for data collection, and propose a novel anonymity-based PPDC method for healthcare services in this paper. The threat model is analyzed in the client-server-to-user (CS2U) model. On client-side, we utilize (a,k)-anonymity notion to generate anonymous tuples which can resist possible attack, and adopt a bottom-up clustering method to create clusters that satisfy a base privacy level of (a,k)-anonymity. On server-side, we reduce the communication cost through generalization technology, and compress (a,k)-anonymous data through an UPGMA-based cluster combination method to make the data meet the deeper level of privacy (a,k)-anonymity (a ≥ a, k ≥ k). Theoretical analysis and experimental results prove that our scheme is effective in privacy-preserving and data quality.
物联网技术在医疗服务中的广泛应用推动了医疗服务的智能化水平。然而，这也给数据采集带来了潜在的隐私威胁。在医疗服务系统中，包含隐私信息的健康和医疗数据经常在网络之间传输，因此需要保护此类隐私信息。因此，需要采用隐私保护数据采集（PPDC）方案来保护客户端（患者）的数据。我们采用（a，k）-匿名模型作为数据采集的隐私保护方案，并在本文中提出了一种新的基于匿名的医疗服务隐私保护数据采集方法。威胁模型在客户端-服务器-用户（CS2U）模型中进行分析。在客户端，我们利用（a，k）-匿名概念生成可以抵御可能攻击的匿名元组，并采用自底向上的聚类方法创建满足基本（a，k）-匿名隐私级别的集群。在服务器端，我们通过泛化技术降低通信成本，并通过基于 UPGMA 的集群组合方法压缩（a，k）-匿名数据，使数据满足更深层次的（a，k）-匿名性（a≥a，k≥k）。理论分析和实验结果证明，我们的方案在隐私保护和数据质量方面是有效的。
