DOTNET PROJECT
Dynamic
Multi-Keyword Ranked Search Based on Bloom Filter Over Encrypted Cloud Data
Abstract:
Cloud computing has
become a popular approach to manage personal data for the economic savings and
management flexibility in recent year. However, the sensitive data must be
encrypted before outsourcing to cloud servers for the consideration of privacy,
which makes some traditional data utilization functions, such as the plaintext
keyword search, impossible. To solve this problem, we present a multi-keyword
ranked search scheme over encrypted cloud data supporting dynamic operations
efficiently. Our scheme utilizes the vector space model combined with TF
$\times $ IDF rule and cosine similarity measure to achieve a multi-keyword
ranked search. However, traditional solutions have to suffer high computational
costs. In order to achieve the sub-linear search time, our scheme introduces
Bloom filter to build a search index tree. What is more, our scheme can support
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. Then, the enhanced scheme is presented later to
guarantee security even under the known background model. The experiments on
the real-world data set show that the performances of our proposed schemes are
satisfactory.
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