ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2017, Vol. 54 ›› Issue (2): 295-304.doi: 10.7544/issn1000-1239.2017.20150751

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A Secure Index Against Statistical Analysis Attacks

Hui Zhen1,2, Feng Dengguo1,3, Zhang Min1, Hong Cheng1   

  1. 1(Laboratory of Trusted Computing and Information Assurance, Institute of Software, Chinese Academy of Sciences, Beijing 100190);2(University of Chinese Academy of Sciences, Beijing 100049);3(State Key Laboratory of Computer Science (Institute of Software, Chinese Academy of Sciences), Beijing 100190)
  • Online:2017-02-01

Abstract: Most of current searchable encryption schemes suffer from the threat of statistical analysis attacks. Some related works design their keyword/document trapdoors in a one-to-one method to avoid the threat, but it could lead to a severe overhead in searching cost. In the present paper, we design an efficient secure index to defend against a kind of statistical analysis attack. This scheme uses a Bloom filter to build indexes for each document. In order to save searching cost, one unique trapdoor is built for one word. To satisfy the security requirement, this scheme treats indexes of all documents as a matrix, and then adopts forged indexes and interpolation to make sure the frequencies of different words are closed and all indexes in the matrix are indistinguishable between each other. As a result, a particular word in the matrix cannot be recognized, thus the statistical analysis attack is resisted. In implementation, this scheme uses inverted indexes to further improve querying performance. The scheme is proved to be semantic security. Experimental results show that the querying performance of our scheme is double of Z-IDX at large dataset and words cannot be recognized based on their frequencies.

Key words: searchable encryption (SE), statistical leakage, inverted index, Bloom filter, access pattern

CLC Number: