PPSDT: A Novel Privacy-Preserving Single Decision Tree Algorithm for Clinical Decision-Support Systems Using IoT Devices
PPSDT: A Novel Privacy-Preserving Single Decision Tree Algorithm for Clinical Decision-Support Systems Using IoT Devices
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DOI：
10.3390/s19010142
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发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Tian, Yuan
中科院分区：
文献类型：
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作者：
Alabdulkarim, Alia;Al-Rodhaan, Mznah;Tian, Yuan
医疗服务提供者为患者提供高质量的服务，以回报患者的信任和满意。医疗保健中的物联网（IoT）提供了不同的解决方案来增强患者-医生体验。临床决策支持系统通过提高诊断速度和准确性来提高卫生服务质量。基于数据挖掘技术和历史病历，建立了患者症状分类模型。在本文中，我们提出了一种基于我们新颖的隐私保护单决策树算法的隐私保护临床决策支持系统，用于诊断新症状，而不会将患者的数据暴露给不同的网络攻击。采用同态加密密码对用户数据进行保护。此外，该算法使用随机数来避免一方解密另一方的数据，因为他们都将使用相同的密钥对。仿真结果表明，该算法的性能优于朴素贝叶斯算法46.46%；除了键值和大小对运行时的影响之外。此外，我们的模型通过证明进行验证，证明满足医院数据集的隐私要求、属性值的频率要求和诊断症状要求。
Medical service providers offer their patients high quality services in return for their trust and satisfaction. The Internet of Things (IoT) in healthcare provides different solutions to enhance the patient-physician experience. Clinical Decision-Support Systems are used to improve the quality of health services by increasing the diagnosis pace and accuracy. Based on data mining techniques and historical medical records, a classification model is built to classify patients' symptoms. In this paper, we propose a privacy-preserving clinical decision-support system based on our novel privacy-preserving single decision tree algorithm for diagnosing new symptoms without exposing patients' data to different network attacks. A homomorphic encryption cipher is used to protect users' data. In addition, the algorithm uses nonces to avoid one party from decrypting other parties' data since they all will be using the same key pair. Our simulation results have shown that our novel algorithm have outperformed the Naive Bayes algorithm by 46.46%; in addition to the effects of the key value and size on the run time. Furthermore, our model is validated by proves, which meet the privacy requirements of the hospitals' datasets, frequency of attribute values, and diagnosed symptoms.