Efficient k-Nearest Neighbor Classification Over Semantically Secure Hybrid Encrypted Cloud Database
Nowadays, individuals and companies increasingly tend to outsource their databases and further data operations to cloud service provides. However, utilizing the cost-saving advantages of cloud computing brings about the risk of violating database security and user's privacy. In this paper, we f...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8419706/ |
| _version_ | 1819179512113397760 |
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| author | Wei Wu Jian Liu Hong Rong Huimei Wang Ming Xian |
| author_facet | Wei Wu Jian Liu Hong Rong Huimei Wang Ming Xian |
| author_sort | Wei Wu |
| collection | DOAJ |
| description | Nowadays, individuals and companies increasingly tend to outsource their databases and further data operations to cloud service provides. However, utilizing the cost-saving advantages of cloud computing brings about the risk of violating database security and user's privacy. In this paper, we focus on the problem of privacy-preserving k-nearest neighbor (kNN) classification, in which a query user (QU) submits an encrypted query point to a cloud server (CS) and asks for the kNN classification labels based on the encrypted cloud database outsourced by a data owner (DO), without disclosing any privacy of DO or QU to CS. Previous secure kNN query schemes either cannot fully achieve required security properties or introduce heavy computation costs, making them not practical in real-world applications. To better solve this problem, we propose a novel efficient privacy-preserving kNN classification protocol over semantically secure hybrid encrypted cloud database using Paillier and ElGamal cryptosystems. The proposed protocol protects both database security and query privacy and also hides data access patterns from CS. We formally analyze the security of our protocol and evaluate the performance through extensive experiments. The experiment results show that the computation cost of our protocol is about two orders of magnitude lower than that of the state-of-the-art protocol while achieving the same security and privacy properties. |
| first_indexed | 2024-12-22T21:59:37Z |
| format | Article |
| id | doaj.art-3a681ddf40d2442e985e23a2c975439f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T21:59:37Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3a681ddf40d2442e985e23a2c975439f2022-12-21T18:11:09ZengIEEEIEEE Access2169-35362018-01-016417714178410.1109/ACCESS.2018.28597588419706Efficient k-Nearest Neighbor Classification Over Semantically Secure Hybrid Encrypted Cloud DatabaseWei Wu0https://orcid.org/0000-0003-1424-1414Jian Liu1Hong Rong2https://orcid.org/0000-0002-5631-8128Huimei Wang3Ming Xian4School of Electronic Science, National University of Defense Technology, Changsha, ChinaSchool of Electronic Science, National University of Defense Technology, Changsha, ChinaSchool of Electronic Science, National University of Defense Technology, Changsha, ChinaSchool of Electronic Science, National University of Defense Technology, Changsha, ChinaSchool of Electronic Science, National University of Defense Technology, Changsha, ChinaNowadays, individuals and companies increasingly tend to outsource their databases and further data operations to cloud service provides. However, utilizing the cost-saving advantages of cloud computing brings about the risk of violating database security and user's privacy. In this paper, we focus on the problem of privacy-preserving k-nearest neighbor (kNN) classification, in which a query user (QU) submits an encrypted query point to a cloud server (CS) and asks for the kNN classification labels based on the encrypted cloud database outsourced by a data owner (DO), without disclosing any privacy of DO or QU to CS. Previous secure kNN query schemes either cannot fully achieve required security properties or introduce heavy computation costs, making them not practical in real-world applications. To better solve this problem, we propose a novel efficient privacy-preserving kNN classification protocol over semantically secure hybrid encrypted cloud database using Paillier and ElGamal cryptosystems. The proposed protocol protects both database security and query privacy and also hides data access patterns from CS. We formally analyze the security of our protocol and evaluate the performance through extensive experiments. The experiment results show that the computation cost of our protocol is about two orders of magnitude lower than that of the state-of-the-art protocol while achieving the same security and privacy properties.https://ieeexplore.ieee.org/document/8419706/Privacy-preservingkNN classificationcloud computingencryption |
| spellingShingle | Wei Wu Jian Liu Hong Rong Huimei Wang Ming Xian Efficient k-Nearest Neighbor Classification Over Semantically Secure Hybrid Encrypted Cloud Database IEEE Access Privacy-preserving kNN classification cloud computing encryption |
| title | Efficient k-Nearest Neighbor Classification Over Semantically Secure Hybrid Encrypted Cloud Database |
| title_full | Efficient k-Nearest Neighbor Classification Over Semantically Secure Hybrid Encrypted Cloud Database |
| title_fullStr | Efficient k-Nearest Neighbor Classification Over Semantically Secure Hybrid Encrypted Cloud Database |
| title_full_unstemmed | Efficient k-Nearest Neighbor Classification Over Semantically Secure Hybrid Encrypted Cloud Database |
| title_short | Efficient k-Nearest Neighbor Classification Over Semantically Secure Hybrid Encrypted Cloud Database |
| title_sort | efficient k nearest neighbor classification over semantically secure hybrid encrypted cloud database |
| topic | Privacy-preserving kNN classification cloud computing encryption |
| url | https://ieeexplore.ieee.org/document/8419706/ |
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