Efficient and privacy-preserving multi-party skyline queries in online medical primary diagnosis
Wireless body area networks enable data collection from wearable devices, thereby allowing online medical primary diagnosis via cloud computing. Data security and diagnosis accuracy are two main concerns in the online medical primary diagnosis system. While traditional solutions can ensure the confi...
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| Format: | Article |
| Language: | English |
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Springer
2023-09-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S131915782300191X |
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| _version_ | 1849305398673145856 |
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| author | Wanjun Hao Shuqin Liu Chunyang Lv Yunling Wang Jianfeng Wang |
| author_facet | Wanjun Hao Shuqin Liu Chunyang Lv Yunling Wang Jianfeng Wang |
| author_sort | Wanjun Hao |
| collection | DOAJ |
| description | Wireless body area networks enable data collection from wearable devices, thereby allowing online medical primary diagnosis via cloud computing. Data security and diagnosis accuracy are two main concerns in the online medical primary diagnosis system. While traditional solutions can ensure the confidentiality of online data, their incapacity to integrate data from multiple users restricts the development of accurate diagnostic models and leads to low accuracy. Recently, a medical preliminary diagnosis scheme with improved accuracy was proposed, which employs skyline computation to construct a precise diagnostic model using multiple medical datasets. However, their scheme requires a trusted third party and experiences excessive query time. To address this issue, we present an Effective and Privacy-preserving Multi-party Skyline diagnosis scheme (EPMS) that offers even higher accuracy and extremely fast diagnosis without trusted third parties. Specifically, we devise several sub-protocols to support secure skyline computation. By integrating our protocols with privacy matrix techniques, the cloud server can generate a comprehensive diagnostic model from multiple data sources, offering accurate diagnosis services without disclosing any users’ personal information. We implement our scheme and conduct extensive experiments, which showed that our approach achieves a speedup of approximately 200× in query time and nearly 20% improvement in accuracy. |
| format | Article |
| id | doaj-art-078693f6feed40dca60be32452a47f5e |
| institution | Kabale University |
| issn | 1319-1578 |
| language | English |
| publishDate | 2023-09-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-078693f6feed40dca60be32452a47f5e2025-08-20T03:55:28ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782023-09-0135810163710.1016/j.jksuci.2023.101637Efficient and privacy-preserving multi-party skyline queries in online medical primary diagnosisWanjun Hao0Shuqin Liu1Chunyang Lv2Yunling Wang3Jianfeng Wang4School of Cyber Engineering, Xidian University, Xi’an, China; Henan Key Laboratory of Network Cryptography Technology, Henan, ChinaSchool of Computer Science & Technology, Xi’an University of Post and Telecommunications, Xi’an, ChinaSchool of Cyber Engineering, Xidian University, Xi’an, ChinaHenan Key Laboratory of Network Cryptography Technology, Henan, China; School of Cyberspace Security, Xi’an University of Posts and Telecommunications, Xi’an, ChinaSchool of Cyber Engineering, Xidian University, Xi’an, China; Corresponding author at: South Campus of Xidian University, 266 Xinglong Section of Xifeng Road, Xi’an, Shaanxi 710126, China.Wireless body area networks enable data collection from wearable devices, thereby allowing online medical primary diagnosis via cloud computing. Data security and diagnosis accuracy are two main concerns in the online medical primary diagnosis system. While traditional solutions can ensure the confidentiality of online data, their incapacity to integrate data from multiple users restricts the development of accurate diagnostic models and leads to low accuracy. Recently, a medical preliminary diagnosis scheme with improved accuracy was proposed, which employs skyline computation to construct a precise diagnostic model using multiple medical datasets. However, their scheme requires a trusted third party and experiences excessive query time. To address this issue, we present an Effective and Privacy-preserving Multi-party Skyline diagnosis scheme (EPMS) that offers even higher accuracy and extremely fast diagnosis without trusted third parties. Specifically, we devise several sub-protocols to support secure skyline computation. By integrating our protocols with privacy matrix techniques, the cloud server can generate a comprehensive diagnostic model from multiple data sources, offering accurate diagnosis services without disclosing any users’ personal information. We implement our scheme and conduct extensive experiments, which showed that our approach achieves a speedup of approximately 200× in query time and nearly 20% improvement in accuracy.http://www.sciencedirect.com/science/article/pii/S131915782300191XSkyline computationMedical primary diagnosisMulti-party computation |
| spellingShingle | Wanjun Hao Shuqin Liu Chunyang Lv Yunling Wang Jianfeng Wang Efficient and privacy-preserving multi-party skyline queries in online medical primary diagnosis Journal of King Saud University: Computer and Information Sciences Skyline computation Medical primary diagnosis Multi-party computation |
| title | Efficient and privacy-preserving multi-party skyline queries in online medical primary diagnosis |
| title_full | Efficient and privacy-preserving multi-party skyline queries in online medical primary diagnosis |
| title_fullStr | Efficient and privacy-preserving multi-party skyline queries in online medical primary diagnosis |
| title_full_unstemmed | Efficient and privacy-preserving multi-party skyline queries in online medical primary diagnosis |
| title_short | Efficient and privacy-preserving multi-party skyline queries in online medical primary diagnosis |
| title_sort | efficient and privacy preserving multi party skyline queries in online medical primary diagnosis |
| topic | Skyline computation Medical primary diagnosis Multi-party computation |
| url | http://www.sciencedirect.com/science/article/pii/S131915782300191X |
| work_keys_str_mv | AT wanjunhao efficientandprivacypreservingmultipartyskylinequeriesinonlinemedicalprimarydiagnosis AT shuqinliu efficientandprivacypreservingmultipartyskylinequeriesinonlinemedicalprimarydiagnosis AT chunyanglv efficientandprivacypreservingmultipartyskylinequeriesinonlinemedicalprimarydiagnosis AT yunlingwang efficientandprivacypreservingmultipartyskylinequeriesinonlinemedicalprimarydiagnosis AT jianfengwang efficientandprivacypreservingmultipartyskylinequeriesinonlinemedicalprimarydiagnosis |