MUSE: An Efficient and Accurate Verifiable Privacy-Preserving Multikeyword Text Search over Encrypted Cloud Data
المؤلفون المشاركون
Dai, Hua
Xiangyang, Zhu
Xun, Yi
Xiao, Li
Yang, Geng
المصدر
Security and Communication Networks
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-17، 17ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-07-11
دولة النشر
مصر
عدد الصفحات
17
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
With the development of cloud computing, services outsourcing in clouds has become a popular business model.
However, due to the fact that data storage and computing are completely outsourced to the cloud service provider, sensitive data of data owners is exposed, which could bring serious privacy disclosure.
In addition, some unexpected events, such as software bugs and hardware failure, could cause incomplete or incorrect results returned from clouds.
In this paper, we propose an efficient and accurate verifiable privacy-preserving multikeyword text search over encrypted cloud data based on hierarchical agglomerative clustering, which is named MUSE.
In order to improve the efficiency of text searching, we proposed a novel index structure, HAC-tree, which is based on a hierarchical agglomerative clustering method and tends to gather the high-relevance documents in clusters.
Based on the HAC-tree, a noncandidate pruning depth-first search algorithm is proposed, which can filter the unqualified subtrees and thus accelerate the search process.
The secure inner product algorithm is used to encrypted the HAC-tree index and the query vector.
Meanwhile, a completeness verification algorithm is given to verify search results.
Experiment results demonstrate that the proposed method outperforms the existing works, DMRS and MRSE-HCI, in efficiency and accuracy, respectively.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Xiangyang, Zhu& Dai, Hua& Xun, Yi& Yang, Geng& Xiao, Li. 2017. MUSE: An Efficient and Accurate Verifiable Privacy-Preserving Multikeyword Text Search over Encrypted Cloud Data. Security and Communication Networks،Vol. 2017, no. 2017, pp.1-17.
https://search.emarefa.net/detail/BIM-1202789
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Xiangyang, Zhu…[et al.]. MUSE: An Efficient and Accurate Verifiable Privacy-Preserving Multikeyword Text Search over Encrypted Cloud Data. Security and Communication Networks No. 2017 (2017), pp.1-17.
https://search.emarefa.net/detail/BIM-1202789
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Xiangyang, Zhu& Dai, Hua& Xun, Yi& Yang, Geng& Xiao, Li. MUSE: An Efficient and Accurate Verifiable Privacy-Preserving Multikeyword Text Search over Encrypted Cloud Data. Security and Communication Networks. 2017. Vol. 2017, no. 2017, pp.1-17.
https://search.emarefa.net/detail/BIM-1202789
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1202789
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر