Towards Differential Query Services in Cost-Efficient Clouds
TLDR
This paper presents three efficient information retrieval for ranked query (EIRQ) schemes to reduce querying overhead incurred on the cloud.Abstract:
Cloud computing as an emerging technology trend is expected to reshape the advances in information technology. In a cost-efficient cloud environment, a user can tolerate a certain degree of delay while retrieving information from the cloud to reduce costs. In this paper, we address two fundamental issues in such an environment: privacy and efficiency. We first review a private keyword-based file retrieval scheme that was originally proposed by Ostrovsky. Their scheme allows a user to retrieve files of interest from an untrusted server without leaking any information. The main drawback is that it will cause a heavy querying overhead incurred on the cloud and thus goes against the original intention of cost efficiency. In this paper, we present three efficient information retrieval for ranked query (EIRQ) schemes to reduce querying overhead incurred on the cloud. In EIRQ, queries are classified into multiple ranks, where a higher ranked query can retrieve a higher percentage of matched files. A user can retrieve files on demand by choosing queries of different ranks. This feature is useful when there are a large number of matched files, but the user only needs a small subset of them. Under different parameter settings, extensive evaluations have been conducted on both analytical models and on a real cloud environment, in order to examine the effectiveness of our schemes.read more
Citations
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Journal ArticleDOI
Cost Efficient Query Services throughAggregation and Distribution Layer
TL;DR: An Efficient Information Retrieval for Ranked Queries (EIRQ) scheme is recovery of ranked files on user demand by working based on the Aggregation and Distribution Layer (ADL).
Journal Article
Ranked Query Assistance in Untrusted Cloud Helper without Reveal Sensitive Data
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Personalized Search with Secure Updates in Educational Resources
TL;DR: A Personalized Search with Secure Updates (PSSU) in educational resources is proposed, able to obtain large educational resources data from resources contributed by the general public by the introduction of Trusted Third-Party (TTP) server, and evaluate the resources by analyzing the user's usage, then the users can obtain high-quality resources that meet their own needs.
Privacy and Secured Multiparty Data Categorization using Cloud Resources
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TL;DR: Back-Propagation Neural network (BPN) is an effective method for learning neural networks that combines Boneh, Goh and Nissim doubly homomorphic encryption algorithm with random sharing algorithm to protect the intermediate data during the learning process.
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