Privacy-Preserving Decentralized Functional Encryption for Inner Product

To support secure data mining and privacy-preserving computation, partial access and selective computation on encrypted data are desirable. Functional encryption (FE) is a new paradigm of public-key encryption and allows authorized users to compute specific functions on encrypted data without knowing the data. However, in some FE schemes, a trusted central authority (CA) is required to generate secret keys for users according to the description of functions. In this paper, to reduce trust on the CA and protect users' privacy, a privacy-preserving decentralized FE for inner product (PPDFEIP) scheme is proposed where multiple authorities co-exist and work independently without any interaction. Especially, to resist collusion attacks, all secret keys of the same user are tied to his/her global identifier (GID), but authorities cannot know any information of the GID even if they collaborate. We formalize the definition and security model of our PPFEIP scheme, and propose a concrete construction. Furthermore, the proposed scheme is implemented and evaluated. Finally, the security of our PPDFEIP scheme is reduced to well-known complexity assumptions. The novelty is to reduce trust on the CA, protect users' privacy and enable authorized users to compute inner product on encrypted data without compromising confidentiality.