A Privacy Preserving Cloud-Based K-NN Search Scheme with Lightweight User Loads
With the growing popularity of cloud computing, it is convenient for data owners to outsource their data to a cloud server. By utilizing the massive storage and computational resources in cloud, data owners can also provide a platform for users to make query requests. However, due to the privacy con...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-01-01
|
| Series: | Computers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-431X/9/1/1 |
| _version_ | 1811187103013273600 |
|---|---|
| author | Yeong-Cherng Hsu Chih-Hsin Hsueh Ja-Ling Wu |
| author_facet | Yeong-Cherng Hsu Chih-Hsin Hsueh Ja-Ling Wu |
| author_sort | Yeong-Cherng Hsu |
| collection | DOAJ |
| description | With the growing popularity of cloud computing, it is convenient for data owners to outsource their data to a cloud server. By utilizing the massive storage and computational resources in cloud, data owners can also provide a platform for users to make query requests. However, due to the privacy concerns, sensitive data should be encrypted before outsourcing. In this work, a novel privacy preserving K-nearest neighbor (K-NN) search scheme over the encrypted outsourced cloud dataset is proposed. The problem is about letting the cloud server find K nearest points with respect to an encrypted query on the encrypted dataset, which was outsourced by data owners, and return the searched results to the querying user. Comparing with other existing methods, our approach leverages the resources of the cloud more by shifting most of the required computational loads, from data owners and query users, to the cloud server. In addition, there is no need for data owners to share their secret key with others. In a nutshell, in the proposed scheme, data points and user queries are encrypted attribute-wise and the entire search algorithm is performed in the encrypted domain; therefore, our approach not only preserves the data privacy and query privacy but also hides the data access pattern from the cloud server. Moreover, by using a tree structure, the proposed scheme could accomplish query requests in sub-liner time, according to our performance analysis. Finally, experimental results demonstrate the practicability and the efficiency of our method. |
| first_indexed | 2024-04-11T13:56:25Z |
| format | Article |
| id | doaj.art-d91a07fc83cf4651988ba0f0ea7ef82f |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-04-11T13:56:25Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-d91a07fc83cf4651988ba0f0ea7ef82f2022-12-22T04:20:18ZengMDPI AGComputers2073-431X2020-01-0191110.3390/computers9010001computers9010001A Privacy Preserving Cloud-Based K-NN Search Scheme with Lightweight User LoadsYeong-Cherng Hsu0Chih-Hsin Hsueh1Ja-Ling Wu2MediaTek Inc., Hsinchu 30078, TaiwanGraduate Institute of Networking and Multimedia, National Taiwan University, Taipei 10617, TaiwanDepartment of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, TaiwanWith the growing popularity of cloud computing, it is convenient for data owners to outsource their data to a cloud server. By utilizing the massive storage and computational resources in cloud, data owners can also provide a platform for users to make query requests. However, due to the privacy concerns, sensitive data should be encrypted before outsourcing. In this work, a novel privacy preserving K-nearest neighbor (K-NN) search scheme over the encrypted outsourced cloud dataset is proposed. The problem is about letting the cloud server find K nearest points with respect to an encrypted query on the encrypted dataset, which was outsourced by data owners, and return the searched results to the querying user. Comparing with other existing methods, our approach leverages the resources of the cloud more by shifting most of the required computational loads, from data owners and query users, to the cloud server. In addition, there is no need for data owners to share their secret key with others. In a nutshell, in the proposed scheme, data points and user queries are encrypted attribute-wise and the entire search algorithm is performed in the encrypted domain; therefore, our approach not only preserves the data privacy and query privacy but also hides the data access pattern from the cloud server. Moreover, by using a tree structure, the proposed scheme could accomplish query requests in sub-liner time, according to our performance analysis. Finally, experimental results demonstrate the practicability and the efficiency of our method.https://www.mdpi.com/2073-431X/9/1/1k-nnprivacy preservingcloudencryptionsecurity |
| spellingShingle | Yeong-Cherng Hsu Chih-Hsin Hsueh Ja-Ling Wu A Privacy Preserving Cloud-Based K-NN Search Scheme with Lightweight User Loads Computers k-nn privacy preserving cloud encryption security |
| title | A Privacy Preserving Cloud-Based K-NN Search Scheme with Lightweight User Loads |
| title_full | A Privacy Preserving Cloud-Based K-NN Search Scheme with Lightweight User Loads |
| title_fullStr | A Privacy Preserving Cloud-Based K-NN Search Scheme with Lightweight User Loads |
| title_full_unstemmed | A Privacy Preserving Cloud-Based K-NN Search Scheme with Lightweight User Loads |
| title_short | A Privacy Preserving Cloud-Based K-NN Search Scheme with Lightweight User Loads |
| title_sort | privacy preserving cloud based k nn search scheme with lightweight user loads |
| topic | k-nn privacy preserving cloud encryption security |
| url | https://www.mdpi.com/2073-431X/9/1/1 |
| work_keys_str_mv | AT yeongchernghsu aprivacypreservingcloudbasedknnsearchschemewithlightweightuserloads AT chihhsinhsueh aprivacypreservingcloudbasedknnsearchschemewithlightweightuserloads AT jalingwu aprivacypreservingcloudbasedknnsearchschemewithlightweightuserloads AT yeongchernghsu privacypreservingcloudbasedknnsearchschemewithlightweightuserloads AT chihhsinhsueh privacypreservingcloudbasedknnsearchschemewithlightweightuserloads AT jalingwu privacypreservingcloudbasedknnsearchschemewithlightweightuserloads |