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Reliable Medical Recommendation Based on Privacy-Preserving Collaborative Filtering

Mengwei Hou1, Rong Wei1,*, Tiangang Wang1, Yu Cheng2, Buyue Qian3

The First Affiliated Hospital of Xi’an Jiaotong University, 277 West Yanta Road, Xi’an 710061, P.R. China.
IBM Research AI, Yorktown Heights, 10593, USA.
Xi’An Jiaotong University, No. 28, Xianning West Road, Xi’an 710061, P.R. China.

* Corresponding Author: Rong Wei. Email: email.

Computers, Materials & Continua 2018, 56(1), 137-149. 10.3970/cmc.2018.02438


Collaborative filtering (CF) methods are widely adopted by existing medical recommendation systems, which can help clinicians perform their work by seeking and recommending appropriate medical advice. However, privacy issue arises in this process as sensitive patient private data are collected by the recommendation server. Recently proposed privacy-preserving collaborative filtering methods, using computation-intensive cryptography techniques or data perturbation techniques are not appropriate in medical online service. The aim of this study is to address the privacy issues in the context of neighborhood-based CF methods by proposing a Privacy Preserving Medical Recommendation (PPMR) algorithm, which can protect patients’ treatment information and demographic information during online recommendation process without compromising recommendation accuracy and efficiency. The proposed algorithm includes two privacy preserving operations: Private Neighbor Selection and Neighborhood-based Differential Privacy Recommendation. Private Neighbor Selection is conducted on the basis of the notion of k-anonymity method, meaning that neighbors are privately selected for the target user according to his/her similarities with others. Neighborhood-based Differential Privacy Recommendation and a differential privacy mechanism are introduced in this operation to enhance the performance of recommendation. Our algorithm is evaluated using the real-world hospital EMRs dataset. Experimental results demonstrate that the proposed method achieves stable recommendation accuracy while providing comprehensive privacy for individual patients.


Cite This Article

APA Style
Hou, M., Wei, R., Wang, T., Cheng, Y., Qian, B. (2018). Reliable medical recommendation based on privacy-preserving collaborative filtering. Computers, Materials & Continua, 56(1), 137-149. 10.3970/cmc.2018.02438
Vancouver Style
Hou M, Wei R, Wang T, Cheng Y, Qian B. Reliable medical recommendation based on privacy-preserving collaborative filtering. Comput Mater Contin. 2018;56(1):137-149 10.3970/cmc.2018.02438
IEEE Style
M. Hou, R. Wei, T. Wang, Y. Cheng, and B. Qian "Reliable Medical Recommendation Based on Privacy-Preserving Collaborative Filtering," Comput. Mater. Contin., vol. 56, no. 1, pp. 137-149. 2018. 10.3970/cmc.2018.02438

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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