A query privacy-enhanced and secure search scheme over encrypted data in cloud computing

With the emerging of the cloud computing, secure search over encrypted cloud data has become a hot research spot. Previous schemes achieve weaker query privacy-preserving ability due to the limitations of query trapdoor generation mechanisms. In these schemes, a data owner usually knows fully well the query contents of data users and a data user can also easily analyze query contents of another data user. In some application scenarios, the data user may be unwilling to leak their query privacy to anyone else except himself. We propose a privacy-enhanced search scheme by allowing the data user to generate random query trapdoor every time. We leverage Bloom filter and bilinear pairing operation to construct secure index for each data file, which enables the cloud to perform search without obtaining any useful information. We prove that our scheme is secure and extensive experiments demonstrate the correctness and practicality of the proposed scheme.