Efficient Multi-keyword Approximate Search on Encrypted Cloud Data
碩士 === 國立臺灣科技大學 === 資訊工程系 === 105 === Now with the growing popularity of cloud computing, data owners outsource the data to cloud for convenience management. Despite the advantages of cloud storage, putting a lot of attention on data security is definitely necessary. Before outsourcing the sensitive...
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ndltd-TW-105NTUS53920532019-05-15T23:46:34Z http://ndltd.ncl.edu.tw/handle/yezd9k Efficient Multi-keyword Approximate Search on Encrypted Cloud Data 加密雲端數據上之多關鍵字近似搜尋 WAN-NI SHIH 施宛妮 碩士 國立臺灣科技大學 資訊工程系 105 Now with the growing popularity of cloud computing, data owners outsource the data to cloud for convenience management. Despite the advantages of cloud storage, putting a lot of attention on data security is definitely necessary. Before outsourcing the sensitive data, data owners should encrypt it for privacy requirements. However, the encryption could make search problem of utilization become difficult by keywords. There are some existing techniques provide keyword search over encrypted data. Those strategies contain weakness about the inefficient searchable vectors. The previous strategies maintain a large number of words as the elements to construct the index for each document. It will cause the amount of calculation that sharply increase. In this thesis, we propose a novel keyword search over encrypted data scheme by reducing the size of the index vectors. First, we analyze data through latent semantic analysis, to discover the association between different words. Then, we select the principal dimension to retain the important information of dataset. After transforming the data from high-dimensional space to low-dimensional space, we provide appropriate result to the user's request. Our proposed scheme support approximate keyword search with premeditate data analysis rather than maintain large number of words. Experiments on real-world dataset show that our scheme not only provides flexibly query for users, but also effectively reduce the amount of calculation. Tai-Lin Chin 金台齡 2017 學位論文 ; thesis 34 en_US |
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碩士 === 國立臺灣科技大學 === 資訊工程系 === 105 === Now with the growing popularity of cloud computing, data owners outsource the data to cloud for convenience management. Despite the advantages of cloud storage, putting a lot of attention on data security is definitely necessary. Before outsourcing the sensitive data, data owners should encrypt it for privacy requirements. However, the encryption could make search problem of utilization become difficult by keywords. There are some existing techniques provide keyword search over encrypted data. Those strategies contain weakness about the inefficient searchable vectors. The previous strategies maintain a large number of words as the elements to construct the index for each document. It will cause the amount of calculation that sharply increase. In this thesis, we propose a novel keyword search over encrypted data scheme by reducing the size of the index vectors. First, we analyze data through latent semantic analysis, to discover the association between different words. Then, we select the principal dimension to retain the important information of dataset. After transforming the data from high-dimensional space to low-dimensional space, we provide appropriate result to the user's request. Our proposed scheme support approximate keyword search with premeditate data analysis rather than maintain large number of words. Experiments on real-world dataset show that our scheme not only provides flexibly query for users, but also effectively reduce the amount of calculation.
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author2 |
Tai-Lin Chin |
author_facet |
Tai-Lin Chin WAN-NI SHIH 施宛妮 |
author |
WAN-NI SHIH 施宛妮 |
spellingShingle |
WAN-NI SHIH 施宛妮 Efficient Multi-keyword Approximate Search on Encrypted Cloud Data |
author_sort |
WAN-NI SHIH |
title |
Efficient Multi-keyword Approximate Search on Encrypted Cloud Data |
title_short |
Efficient Multi-keyword Approximate Search on Encrypted Cloud Data |
title_full |
Efficient Multi-keyword Approximate Search on Encrypted Cloud Data |
title_fullStr |
Efficient Multi-keyword Approximate Search on Encrypted Cloud Data |
title_full_unstemmed |
Efficient Multi-keyword Approximate Search on Encrypted Cloud Data |
title_sort |
efficient multi-keyword approximate search on encrypted cloud data |
publishDate |
2017 |
url |
http://ndltd.ncl.edu.tw/handle/yezd9k |
work_keys_str_mv |
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