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Journal of Healthcare Engineering
Volume 2018, Article ID 4073103, 11 pages
https://doi.org/10.1155/2018/4073103
Research Article

Privacy Preserving k-Nearest Neighbor for Medical Diagnosis in e-Health Cloud

Graduate School of Information Security, Korea University, Seoul, Republic of Korea

Correspondence should be addressed to Dong Hoon Lee; rk.ca.aerok@eelhgnod

Received 20 April 2018; Accepted 28 August 2018; Published 15 October 2018

Academic Editor: Ana Margarida Ferreira

Copyright © 2018 Jeongsu Park and Dong Hoon Lee. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Cloud computing is highly suitable for medical diagnosis in e-health services where strong computing ability is required. However, in spite of the huge benefits of adopting the cloud computing, the medical diagnosis field is not yet ready to adopt the cloud computing because it contains sensitive data and hence using the cloud computing might cause a great concern in privacy infringement. For instance, a compromised e-health cloud server might expose the medical dataset outsourced from multiple medical data owners or infringe on the privacy of a patient inquirer by leaking his/her symptom or diagnosis result. In this paper, we propose a medical diagnosis system using e-health cloud servers in a privacy preserving manner when medical datasets are owned by multiple data owners. The proposed system is the first one that achieves the privacy of medical dataset, symptoms, and diagnosis results and hides the data access pattern even from e-health cloud servers performing computations using the data while it is still robust against collusion of the entities. As a building block of the proposed diagnosis system, we design a novel privacy preserving protocol for finding the k data with the highest similarity (PE-FTK) to a given symptom. The protocol reduces the average running time by 35% compared to that of a previous work in the literature. Moreover, the result of the previous work is probabilistic, i.e., the result can contain some error, while the result of our PE-FTK is deterministic, i.e., the result is correct without any error probability.