当前位置: X-MOL 学术ACM Trans. Internet Technol. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
EPRT: An Efficient Privacy-Preserving Medical Service Recommendation and Trust Discovery Scheme for eHealth System
ACM Transactions on Internet Technology ( IF 5.3 ) Pub Date : 2020-07-07 , DOI: 10.1145/3397678
Cong Peng 1 , Debiao He 2 , Jianhua Chen 3 , Neeraj Kumar 4 , Muhammad Khurram Khan 5
Affiliation  

As one of the essential applications of health information technology, the eHealth system plays a significant role in enabling various internet medicine service scenes, most of which primarily rely on service recommendation or an evaluation mechanism. To avoid privacy leakage, some privacy-preserving mechanisms must be adopted to protect raters’ privacy and make evaluation trust reliable. To tackle this challenge, this article proposes an efficient service recommendation and evaluation scheme, called EPRT , which is based on a similarity calculation and trust discovery method. This scheme uses homomorphic encryption technology to encrypt the sensitive data and combines the threshold mechanism and double-trap mechanism to realize the secure computing on the encrypted data, so as to ensure that the plaintexts of the final calculation results (e.g., recommendation value and evaluation truth) are only obtained by the authorized subject. In addition, a detailed security analysis shows that the proposed EPRT scheme can achieve the expected security. In addition, performance comparison results are carried out, demonstrating its effectiveness and accuracy.

中文翻译:

EPRT:电子卫生系统的有效隐私保护医疗服务推荐和信任发现方案

作为健康信息技术的重要应用之一,eHealth系统在赋能各种互联网医疗服务场景中发挥着重要作用,其中大部分主要依靠服务推荐或评估机制。为了避免隐私泄露,必须采用一些隐私保护机制来保护评估者的隐私,使评估信任可靠。为了应对这一挑战,本文提出了一种高效的服务推荐和评估方案,称为EPRT,它基于相似度计算和信任发现方法。该方案采用同态加密技术对敏感数据进行加密,结合门限机制和双重陷阱机制,实现对加密数据的安全计算,保证最终计算结果的明文(如推荐值和评价)真相)仅由授权主体获得。此外,详细的安全分析表明,所提出的EPRT方案可以达到预期的安全性。此外,还进行了性能比较结果,证明了其有效性和准确性。
更新日期:2020-07-07
down
wechat
bug