Enabling Efficient and Privacy-Preserving Health Query Over Outsourced Cloud

With the pervasiveness of Body Sensor Network (BSN) and cloud computing, online health query service has attracted considerable attention and become a promising approach to improve our quality of healthcare service. However, it still faces many challenges on privacy of users' sensitive personal...

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Main Authors: Guoming Wang, Rongxing Lu, Yong Liang Guan
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8529258/
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spelling doaj-687522366515420b927a818bb1ebb5d22021-03-29T21:33:14ZengIEEEIEEE Access2169-35362018-01-016708317084210.1109/ACCESS.2018.28802208529258Enabling Efficient and Privacy-Preserving Health Query Over Outsourced CloudGuoming Wang0https://orcid.org/0000-0003-3131-6916Rongxing Lu1https://orcid.org/0000-0001-5720-0941Yong Liang Guan2School of Electrical and Electronic Engineering, Nanyang Technological University, SingaporeFaculty of Computer Science, University of New Brunswick, Fredericton, CanadaSchool of Electrical and Electronic Engineering, Nanyang Technological University, SingaporeWith the pervasiveness of Body Sensor Network (BSN) and cloud computing, online health query service has attracted considerable attention and become a promising approach to improve our quality of healthcare service. However, it still faces many challenges on privacy of users' sensitive personal information, confidentiality of health service provider's diagnosis model, accuracy of the diagnosis result, and efficiency of the query result. In this paper, we propose an efficient and privacy-preserving health query scheme over outsourced cloud named HeOC. In the HeOC scheme, the authenticated users can send the encrypted physiological data to the cloud and query the specific disease level accurately on the encrypted medical data stored in the cloud. To reduce the query latency, we fist design a sensor anomaly detection technique to find the high risk disease according to the user's sensor information. Then, with the oblivious pseudorandom function protocol, the user queries the diagnosis result accurately. Detailed security analysis shows that the HeOC scheme can achieve the diagnosis without disclosing the privacy of the user's health information and confidentiality of the health service provider's diagnosis model. In addition, the extensive experiments with an android app and two python programs demonstrate its efficiency in computations and communications.https://ieeexplore.ieee.org/document/8529258/Health queryoutsourced cloudprivacysensorsmart phone
collection DOAJ
language English
format Article
sources DOAJ
author Guoming Wang
Rongxing Lu
Yong Liang Guan
spellingShingle Guoming Wang
Rongxing Lu
Yong Liang Guan
Enabling Efficient and Privacy-Preserving Health Query Over Outsourced Cloud
IEEE Access
Health query
outsourced cloud
privacy
sensor
smart phone
author_facet Guoming Wang
Rongxing Lu
Yong Liang Guan
author_sort Guoming Wang
title Enabling Efficient and Privacy-Preserving Health Query Over Outsourced Cloud
title_short Enabling Efficient and Privacy-Preserving Health Query Over Outsourced Cloud
title_full Enabling Efficient and Privacy-Preserving Health Query Over Outsourced Cloud
title_fullStr Enabling Efficient and Privacy-Preserving Health Query Over Outsourced Cloud
title_full_unstemmed Enabling Efficient and Privacy-Preserving Health Query Over Outsourced Cloud
title_sort enabling efficient and privacy-preserving health query over outsourced cloud
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2018-01-01
description With the pervasiveness of Body Sensor Network (BSN) and cloud computing, online health query service has attracted considerable attention and become a promising approach to improve our quality of healthcare service. However, it still faces many challenges on privacy of users' sensitive personal information, confidentiality of health service provider's diagnosis model, accuracy of the diagnosis result, and efficiency of the query result. In this paper, we propose an efficient and privacy-preserving health query scheme over outsourced cloud named HeOC. In the HeOC scheme, the authenticated users can send the encrypted physiological data to the cloud and query the specific disease level accurately on the encrypted medical data stored in the cloud. To reduce the query latency, we fist design a sensor anomaly detection technique to find the high risk disease according to the user's sensor information. Then, with the oblivious pseudorandom function protocol, the user queries the diagnosis result accurately. Detailed security analysis shows that the HeOC scheme can achieve the diagnosis without disclosing the privacy of the user's health information and confidentiality of the health service provider's diagnosis model. In addition, the extensive experiments with an android app and two python programs demonstrate its efficiency in computations and communications.
topic Health query
outsourced cloud
privacy
sensor
smart phone
url https://ieeexplore.ieee.org/document/8529258/
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