Function Private Predicate Encryption for Low Min-Entropy Predicates

Abstract

In this work, we propose new constructions for zero inner-product encryption (ZIPE) and non-zero inner-product encryption (NIPE) from prime-order bilinear pairings, which are both attribute and function private in the public-key setting. Our ZIPE scheme is adaptively attribute private under the standard Matrix DDH assumption for unbounded collusions. It is additionally computationally function private under a min-entropy variant of the Matrix DDH assumption for predicates sampled from distributions with super-logarithmic min-entropy. Existing (statistically) function private ZIPE schemes due to Boneh et al. [Crypto’13, Asiacrypt’13] necessarily require predicate distributions with significantly larger min-entropy in the public-key setting.Our NIPE scheme is adaptively attribute private under the standard Matrix DDH assumption, albeit for bounded collusions. In addition, it achieves computational function privacy under a min-entropy variant of the Matrix DDH assumption for predicates sampled from distributions with super-logarithmic min-entropy. To the best of our knowledge, existing NIPE schemes from bilinear pairings were neither attribute private nor function private. Our constructions are inspired by the linear FE constructions of Agrawal et al. [Crypto’16] and the simulation secure ZIPE of Wee [TCC’17]. In our ZIPE scheme, we show a novel way of embedding two different hard problem instances in a single secret key - one for unbounded collusion-resistance and the other for function privacy. For NIPE, we introduce new techniques for simultaneously achieving attribute and function privacy. We further show that the two constructions naturally generalize to a wider class of predicate encryption schemes such as subspace membership, subspace non-membership and hidden-vector encryption.