دانلود مقاله ISI انگلیسی شماره 74212
عنوان فارسی مقاله

پرس و جوی رتبه چند کلمه کلیدی کارآمد بر روی داده های رمزگذاری شده در رایانش ابری

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
74212 2014 12 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Efficient multi-keyword ranked query over encrypted data in cloud computing
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Future Generation Computer Systems, Volume 30, January 2014, Pages 179–190

کلمات کلیدی
رایانش ابری؛ پرس و جوی چند کلمه کلیدی - پرس و جوی رتبه - پرس و جوهای Top-kk؛ رمزگذاری داده ها؛ حفظ حریم خصوصی
پیش نمایش مقاله
پیش نمایش مقاله پرس و جوی رتبه چند کلمه کلیدی کارآمد بر روی داده های رمزگذاری شده در رایانش ابری

چکیده انگلیسی

Cloud computing infrastructure is a promising new technology and greatly accelerates the development of large scale data storage, processing and distribution. However, security and privacy become major concerns when data owners outsource their private data onto public cloud servers that are not within their trusted management domains. To avoid information leakage, sensitive data have to be encrypted before uploading onto the cloud servers, which makes it a big challenge to support efficient keyword-based queries and rank the matching results on the encrypted data. Most current works only consider single keyword queries without appropriate ranking schemes. In the current multi-keyword ranked search approach, the keyword dictionary is static and cannot be extended easily when the number of keywords increases. Furthermore, it does not take the user behavior and keyword access frequency into account. For the query matching result which contains a large number of documents, the out-of-order ranking problem may occur. This makes it hard for the data consumer to find the subset that is most likely satisfying its requirements. In this paper, we propose a flexible multi-keyword query scheme, called MKQE to address the aforementioned drawbacks. MKQE greatly reduces the maintenance overhead during the keyword dictionary expansion. It takes keyword weights and user access history into consideration when generating the query result. Therefore, the documents that have higher access frequencies and that match closer to the users’ access history get higher rankings in the matching result set. Our experiments show that MKQE presents superior performance over the current solutions.

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