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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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 |
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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 |
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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. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Wang, Guoming Lu, Rongxing Guan, Yong Liang |
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Article |
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Wang, Guoming Lu, Rongxing Guan, Yong Liang |
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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 |
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https://hdl.handle.net/10356/103343 http://hdl.handle.net/10220/47292 |
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1681043889813716992 |