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 info...

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Main Authors: Wang, Guoming, Lu, Rongxing, Guan, Yong Liang
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2019
Subjects:
Online Access:https://hdl.handle.net/10356/103343
http://hdl.handle.net/10220/47292
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1033432020-03-07T14:00:36Z Enabling efficient and privacy-preserving health query over outsourced cloud Wang, Guoming Lu, Rongxing Guan, Yong Liang School of Electrical and Electronic Engineering Outsourced Cloud Health Query DRNTU::Engineering::Electrical and electronic engineering 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. Published version 2019-01-02T02:25:49Z 2019-12-06T21:10:32Z 2019-01-02T02:25:49Z 2019-12-06T21:10:32Z 2018 Journal Article Wang, G., Lu, R., & Guan, Y. L. (2018). Enabling efficient and privacy-preserving health query over outsourced cloud. IEEE Access, 6, 70831-70842. doi:10.1109/ACCESS.2018.2880220 https://hdl.handle.net/10356/103343 http://hdl.handle.net/10220/47292 10.1109/ACCESS.2018.2880220 en IEEE Access © 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. 12 p. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Outsourced Cloud
Health Query
DRNTU::Engineering::Electrical and electronic engineering
spellingShingle Outsourced Cloud
Health Query
DRNTU::Engineering::Electrical and electronic engineering
Wang, Guoming
Lu, Rongxing
Guan, Yong Liang
Enabling efficient and privacy-preserving health query over outsourced cloud
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.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Wang, Guoming
Lu, Rongxing
Guan, Yong Liang
format Article
author Wang, Guoming
Lu, Rongxing
Guan, Yong Liang
author_sort Wang, Guoming
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
publishDate 2019
url https://hdl.handle.net/10356/103343
http://hdl.handle.net/10220/47292
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