Privacy-Preserving Outsourced Inner Product Computation on Encrypted Database

We consider an outsourced computation model in the selective data sharing setting. Specifically, one of the data owners outsources the encrypted data to an untrusted cloud server, and wants to share the specific function of these data with a group of data users. A data user can perform the specific computation on the data that it is authorized to access. We propose a construction under this model for the inner product computation by using the Inner Product Functional Encryption (IPFE) as a building block. A standard IPFE used on this model has two privacy weaknesses regarding the master secret key and the encrypted vector. We propose a strengthened IPFE that revises these weaknesses. We construct a new IPFE scheme and use it to construct an efficient outsourced inner product computation scheme. In our outsourced computation scheme, the storage overhead and the computation cost for a data user are independent of the vector size. The result privacy and the outsourced data privacy are well preserved against the untrusted cloud server. The experimental results show that our schemes are efficient and practical.