Fast and Scalable Range Query Processing With Strong Privacy Protection for Cloud Computing - 2016

PROJECT TITLE:

Fast and Scalable Range Query Processing With Strong Privacy Protection for Cloud Computing - 2016

ABSTRACT:

Privacy has been the key road block to Cloud Computing as clouds may not be totally trusted. This paper is concerned with the matter of privacy-preserving vary question processing on clouds. Prior schemes are weak in privacy protection as they cannot achieve index indistinguishability, and thus enable the cloud to statistically estimate the values of knowledge and queries using domain data and history question results. During this paper, we have a tendency to propose the primary range question processing theme that achieves index indistinguishability beneath the indistinguishability against chosen keyword attack (IND-CKA). Our key plan is to prepare indexing parts in a very complete binary tree referred to as PBtree, which satisfies structure indistinguishability (i.e., two sets of information items have the identical PBtree structure if and only if the 2 sets have the identical number of knowledge items) and node indistinguishability (i.e., the values of PBtree nodes are fully random and have no statistical meaning). We have a tendency to prove that our scheme is secure under the widely adopted IND-CKA security model. We tend to propose two algorithms, specifically PBtree traversal width minimization and PBtree traversal depth minimization, to enhance query processing potency. We tend to prove that the worst-case complexity of our question processing algorithm using PBtree is O(|R|logn), where n is the full number of information items and R is that the set of information things in the query result. We tend to implemented and evaluated our theme on a true-world dataset with five million things. For example, for a question whose results contain 10 data items, it takes only 0.17 ms.

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