ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2017, Vol. 54 ›› Issue (2): 295-304.doi: 10.7544/issn1000-1239.2017.20150751

• 信息安全 • 上一篇    下一篇

一种可抵抗统计攻击的安全索引

惠榛1,2,冯登国1,3,张敏1,洪澄1   

  1. 1(中国科学院软件研究所可信计算与信息保证实验室 北京 1000190); 2(中国科学院大学 北京 100049); 3(计算机科学国家重点实验室(中国科学院软件研究所) 北京 100190) (huizhen@tca.iscas.ac.cn)
  • 出版日期: 2017-02-01
  • 基金资助: 
    国家自然科学基金重点项目(61230005);国家自然科学基金项目(61402456)

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

摘要: 现有的大部分可检索加密方案建立的安全索引面临着统计攻击的威胁.为了抵抗统计攻击,部分方案设计出关键词/文档一一对应的陷门,以检索时多次的陷门计算为代价保证安全性,但是这样又导致检索速度过于慢而无法接受.为此,研究了针对密文的安全检索方案,在克服已有方案缺点的同时保证对于统计攻击的安全性.该方案使用Bloom过滤器为文档的关键词构造索引.为了确保检索效率,对于相同的关键词构造唯一对应的陷门.通过增加伪造的文档索引,并且在索引中进行插值来确保每个关键词在文档集合中出现的次数相似,从而达到语义安全并且能够抵抗统计攻击.在实现中,对索引进行倒排进一步提高检索效率.证明了本方案的安全性,且采用实验验证了其有效性和高效性.

关键词: 可检索加密, 统计泄露, 倒排索引, Bloom过滤器, 访问模式

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

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