A Parallel Multi-Keyword Top-k Search Scheme over Encrypted Cloud Data

EasyChair Preprint 1365

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 propose parallel multi-keyword top-k search algorithms. Security analysis and experiment evaluation show that the proposed scheme is privacy-preserving, efficient and scalable.

Keyphrases: Cloud Computing, Map Reduce, Multi-keyword, Multi-keywords top-k Search, Posting list, TF-IDF Model, distributed computing framework, document set, encrypted cloud data, fragment based encrypted inverted index, inverted index, keyword ranked search, keyword search, parallel computing, privacy preserving, searchable encryption, top-k search

@booklet{EasyChair:1365,
  author    = {Maohu Yang and Hua Dai and Jingjing Bao and Xun Yi and Geng Yang},
  title     = {A Parallel Multi-Keyword Top-k Search Scheme over Encrypted Cloud Data},
  howpublished = {EasyChair Preprint 1365},
  year      = {EasyChair, 2019}}