基于计数型布隆过滤器的可排序密文检索方法

云计算环境下密文检索困难，已有的可搜索加密方案存在时间效率低、文件检索索引不支持更新、检索结果不能实现按精确度排序等问题。首先基于计数型布隆过滤器构建文件检索索引，将文件集中的关键词哈希映射到计数型布隆过滤器索引向量，实现了按关键词进行密文检索，同时，支持密文检索索引的动态更新。其次，由于计数型布隆过滤器本身不具备语义功能，不能实现按相关度对检索结果排序，引入关键词频率矩阵和词频逆文本频率（TF-IDF）模型计算关键词的相关度分值，以实现按相关度分值对检索结果排序。最后，理论和实验性能分析证明了该方法的安全性、可更新能力、可排序能力和高效性。

Ciphertext retrieval ranking method based on counting Bloom filter

It is difficult to retrieve ciphertext in cloud computing. Existing searchable encryption schemes have low time efficiency, which file retrieval index does not support update, and retrieval results cannot be sorted accurately. To solve these problems, firstly, file retrieval index was constructed based on counting Bloom filter, through hash mapping files keywords to counting Bloom filter index vector, to realize ciphertext retrieval with keywords, and support updating of the ciphertext retrieval index. Secondly, because the counting Bloom filter does not have semantic function, it cannot achieve the ranking of retrieval results according to the relevance scores of the keywords. in this paper, the relevance scores of keywords were computed by using keyword frequency matrix and Term Frequency-Inverse Document Frequency (TF-IDF) model, to achieve the ranking function of retrieval results with the relevance score. Finally, theoretical and experimental performance analysis show that, this proposed method is secure, updatable, sortable, and efficient.