Private predictive analysis on encrypted medical data
Private predictive analysis on encrypted medical data
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DOI：
10.1016/j.jbi.2014.04.003
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发表时间：
2014-08-01
影响因子：
4.5
通讯作者：
Naehrig, Michael
中科院分区：
文献类型：
--
作者：
Bos, Joppe W.;Lauter, Kristin;Naehrig, Michael
越来越多的机密医疗记录被存储在医院或大公司托管的数据中心。随着用于对医疗数据进行预测分析的复杂算法的不断开发，将来可能会对私人患者数据进行越来越多的计算。虽然加密提供了一种确保医疗信息隐私的工具，但它限制了对此类数据进行操作的功能。今天使用的传统加密方法只提供非常有限的可能性，或者根本不提供在不首先解密的情况下对加密数据进行操作的可能性。同态加密提供了一种工具来处理加密数据上的这种计算，而不解密数据，甚至不需要解密key.In本文中，我们讨论了可能的应用场景同态加密，以确保敏感的医疗数据的隐私。我们描述了如何使用同态加密对加密数据进行私下预测分析任务。作为概念的证明，我们提出了一个在云中运行的预测服务的工作实现（托管在微软的Windows Azure上），它将私人加密的健康数据作为输入，并以加密的形式返回患心血管疾病的概率。由于云服务使用同态加密，它在只处理加密数据的同时进行预测，对提交的机密医疗数据一无所知。(C)2014爱思唯尔公司All rights reserved.
Increasingly, confidential medical records are being stored in data centers hosted by hospitals or large companies. As sophisticated algorithms for predictive analysis on medical data continue to be developed, it is likely that, in the future, more and more computation will be done on private patient data. While encryption provides a tool for assuring the privacy of medical information, it limits the functionality for operating on such data. Conventional encryption methods used today provide only very restricted possibilities or none at all to operate on encrypted data without decrypting it first. Homomorphic encryption provides a tool for handling such computations on encrypted data, without decrypting the data, and without even needing the decryption key.In this paper, we discuss possible application scenarios for homomorphic encryption in order to ensure privacy of sensitive medical data. We describe how to privately conduct predictive analysis tasks on encrypted data using homomorphic encryption. As a proof of concept, we present a working implementation of a prediction service running in the cloud (hosted on Microsoft's Windows Azure), which takes as input private encrypted health data, and returns the probability for suffering cardiovascular disease in encrypted form. Since the cloud service uses homomorphic encryption, it makes this prediction while handling only encrypted data, learning nothing about the submitted confidential medical data. (C) 2014 Elsevier Inc. All rights reserved.