基于计数布隆过滤器的属性基多关键词可搜索加密方案

doi:10.11959/j.issn.1000-0801.2023237

Abstract

Abstract:

To address the problems of low retrieval efficiency of searchable encryption scheme under traditional public key cryptosystem, high storage overhead of keyword index, and index does not support dynamic update, based on the good space and time efficiency of counting Bloom filter to determine whether an element belongs to a certain set, the feature into searchable encryption was applied to establish keyword index, thus, the keyword set was mapped into a binary vector, thereby reducing the keyword storage cost.Simultaneously, the feature of counting Bloom filter can be deleted was used to realize the dynamic update of keyword index.In addition, to prevent adversaries from inferring keyword plaintext information from binary vectors through statistical analysis, the binary vector was blinded by permutation, and then the permutation was encrypted by attribute-based encryption for secret sharing.After legitimate users generated keyword traps correctly, the service provider helped users complete keyword matching queries based on the user’s keyword trapdoor.Finally, the security and efficiency of the scheme were proved through security analysis and simulation experiments.

Key words: searchable encryption, counting Bloom filter, attribute-based encryption, permutation

CLC Number:

TP393

Zhidan LI, Yongqun CHEN, Wei WANG. Attribute-based multi-keyword searchable encryption scheme based on counting Bloom filters[J]. Telecommunications Science, 2023, 39(11): 116-127.

Figures/Tables 14

符号	含义
S	用户属性集
SK_S	属性加密密钥
sk	对称加密密钥
W_f	f 的关键词集合
W _f＇	f 的更新后关键词集合
$\tilde{W}$	查询关键词集合
$T$	访问控制树
σ	置换
$T_{W^{*}}$	W ^*对应陷门
C	f 对应对称加密密文
CT₁	σ和sk对应公钥密文
CT₂	W 对应密文索引
n	W 中的关键词个数
t	W ^*中的关键词个数
Y	$T$ 中的叶子节点集合
$\| Y \|$	$T$ 中的叶子节点个数
s	访问控制树根节点处的秘密共享私密值
m	计数布隆过滤器向量长度
l	数值n的比特长度

功能	文献[10]方案	文献[14]方案	本文方案
密钥存储	$(1 + 2 \| S \|) \cdot \| G \|$	$(1 + 2 \| S \|) \cdot \| G \|$	$(1 + 2 \| S \|) \cdot \| G \|$
密文存储	$(3 + n + 2 \| Y \|) \cdot \| G \|$	$(2 + 2 \| Y \|) \cdot \| G \| + 8 m \cdot bit$	$(2 + 2 \| Y \|) \cdot \| G \| + 8 m \cdot bit$
关键词陷门存储	$(3 + 2 \| S \|) \cdot \| G \|$	≤ ntl	≤ ntl
注：l表示数值n的比特长度。

References 15

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对比项	文献[10]方案	文献[14]方案	本文方案
数据访问控制	√	√	√
密文计算效率高	×	√	√
关键词索引更新	×	×	√
关键词存储代价低	×	√	√

功能	文献[10]方案	文献[14]方案	本文方案
密钥计算	(2+2\|S \|)·E	(2+2\|S \|)·E	(2+2\|S \|)·E
密文计算	(3+n+2\|Y\|)E+\|Y\|H ₁+nH₂	(2+2\|Y\|)E+\|Y\|H ₁+nkH+σ	(2+2\|Y\|)E+\|Y\|H₁+nkH+σ
关键词陷门计算	(3+2\|S\|)E+tH₂	ktH +σ	ktH +σ
密文更新	—	—	nkH +σ
解密和检索匹配	(4+2\|S\|)P+\|S\|E	(1+2\|S\|)P+\|S\|E	(1+2\|S\|)P+\|S\|E

Attribute-based multi-keyword searchable encryption scheme based on counting Bloom filters

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