Abstract

With searchable encryptions in the cloud computing, users can outsource their sensitive data in ciphertext to the cloud that provides efficient and privacy-preserving multi-keyword top-k searches. However, most existing top-k search schemes over encrypted cloud data are the centralize schemes which are limited in large scale data environment. To support scalable searches, we propose a parallel multi-keyword top-k search scheme over encrypted cloud data. In this scheme, the fragment-based encrypted inverted index is designed, which is indistinguishable and can be used for parallel searching. On the basis of such indexes, a Map-Reduce-based distributed computing framework is adopted to implement the parallel multi-keyword top-k search algorithms. Security analysis and experiment evaluation show that the proposed scheme is privacy-preserving, efficient and scalable.

Highlights

  • With the rapid development of computer technology and internet application, data in many areas are growing exponentially, the demand for large and scalable storage and computation is becoming urgent

  • We propose a parallel privacy-preserving top-k search (PPTS) scheme, which can meet the requirements of large scale data

  • The contributions of this paper are: 1) We present the fragment-based encrypted inverted index model which is indistinguishable through adding random paddings

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Summary

Introduction

With the rapid development of computer technology and internet application, data in many areas are growing exponentially, the demand for large and scalable storage and computation is becoming urgent. If a scheme calculates the relevance scores between every document and the search keywords, it will cost a large amount of time and computing resources. Jiang et al [9] proposed a secure ciphertext search scheme based on the inverted index, which avoids calculating the relevance scores of irrelevant documents. The indexes of those schemes should be kept in integrity and cannot be segmented, which limits their scalability To conquer such limitation, we propose a parallel privacy-preserving top-k search (PPTS) scheme, which can meet the requirements of large scale data. The Map-Reduce-based distributed computing framework is adopted and the parallel multi-keyword top-k search algorithms are proposed. 2) By adopting the Map-Reduce-based distributed computing framework, the parallel multi-keyword top-k search algorithms are proposed. The result shows that the proposed scheme can realize parallel search while preserving data privacy

Notations and Preliminaries
System Model
Problem Description
Search Framework
Fragment-based Encrypted Inverted Indexes Model
Data Preprocessing and Outsourcing
Map-Reduce-based Top-k Search
SECURITY ANALYSIS
PERFORMANCE EVALUATION
Findings
CONCLUSION
Full Text
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