Dynamic Multi-keyword Ranked Search Based On Bloom Filter Over Encrypted Cloud Data

Dynamic Multi-Keyword Ranked Search Based on Bloom Filter Over Encrypted Cloud Data

Abstract

Cloud computing is an on-demand availability of computer system resources, especially data storage and computing power, without direct active management by the user. The term is generally used to describe data centers available to many users over the Internet. Sensitive data must be encrypted before outsourcing to cloud servers to take into account privacy, which makes some traditional data usage functions such as plain text keyword search impossible. Now, we present a multi-keyword rank search scheme on encrypted cloud data that efficiently supports dynamic operations, to overcome an existing problem. Our scheme uses the vector space model combined with the TF × IDF rule and the cosine similarity measure to achieve a multi-keyword rank search. However, traditional solutions may face higher computational costs. To achieve sub-linear search time, our scheme introduces the Bloom filter to build the search index tree. It supports dynamic operation properly and effectively on the account of the property of the Bloom filter, which means that the updating cost of our scheme is lower than other schemes. We present our basic scheme first, which is secure under the known ciphertext model.