With the flourishing of ubiquitous healthcare and cloud computing technologies, medical primary diagnosis system, which forms a critical capability to link big data analysis technologies with medical knowledge, has shown great potential in improving the quality of healthcare services. However, it still faces many severe challenges on both users’ medical privacy and intellectual property of healthcare service providers, which deters the wide adoption of medical primary diagnosis system. In this paper, we propose an efficient and privacy-preserving medical primary diagnosis framework (CAMPS). Within CAMPS framework, the precise diagnosis models are outsourced to the cloud server in an encrypted manner, and users can access accurate medical primary diagnosis service timely without divulging their medical data. Specifically, based on partially decryption and secure comparison techniques, a special fast secure two-party vector dominance scheme over ciphertext is proposed, with which CAMPS achieves privacy preservation of user’s query and the diagnosis result, as well as the confidentiality of diagnosis models in the outsourced cloud server. Through extensive analysis, we show that CAMPS can ensure that users’ medical data and healthcare service provider’s diagnosis model are kept confidential, and has significantly reduce computation and communication overhead. In addition, performance evaluations via implementing CAMPS demonstrate its effectiveness in term of the real environment.

随着无处不在的医疗保健和云计算技术的蓬勃发展，医疗基本诊断系统已成为将大数据分析技术与医学知识联系起来的关键能力，在提高医疗服务质量方面显示出巨大潜力。但是，它在用户的医疗隐私和医疗服务提供商的知识产权方面仍然面临许多严峻挑战，这阻碍了医疗基础诊断系统的广泛采用。在本文中，我们提出了一个艾菲Ç ient和私法一个CY保留米edical p rimary diagno小号是框架（CAMPS）。在CAMPS框架内，精确的诊断模型以加密的方式外包给云服务器，用户可以及时访问准确的医学主要诊断服务，而无需泄露他们的医学数据。具体而言，基于部分解密和安全比较技术，提出了一种特殊的基于密文的快速安全的两方矢量优势方案，CAMPS可以实现用户查询和诊断结果的隐私保护，以及对诊断模型的保密性。外包的云服务器。通过广泛的分析，我们表明CAMPS可以确保对用户的医疗数据和医疗服务提供商的诊断模型进行保密，并显着减少了计算和通信开销。此外，