Group encryption (GE) is a fundamental privacy-preserving primitive analog of group signatures, which allows users to decrypt specific ciphertexts while hiding themselves within a crowd. Since its first birth, numerous constructions have been proposed, among which the schemes separately constructed by Libert et al. (Asiacrypt 2016) over lattices and by Nguyen et al. (PKC 2021) over coding theory are post-quantum secure. Though the last scheme, at the first time, achieved the full dynamicity (allowing group users to join or leave the group in their ease) and message filtering policy, which greatly improved the state-of-affairs of GE systems, its practical applications are still limited due to the rather complicated design, inefficiency and the weaker security (secure in the random oracle model). In return, the Libert et al.’s scheme possesses a solid security (secure in the standard model), but it lacks the previous functions and still suffers from inefficiency because of extremely using lattice trapdoors. In this work, we re-formalize the model and security definitions of fully dynamic group encryption (FDGE) that are essentially equivalent to but more succinct than Nguyen et al.’s; Then, we provide a generic and efficient zero-knowledge proof method for proving that a binary vector is non-zero over lattices, on which a proof for the Prohibitive message filtering policy in the lattice setting is first achieved (yet in a simple manner); Finally, by combining appropriate cryptographic materials and our presented zero-knowledge proofs, we achieve the first lattice-based FDGE scheme in a simpler manner, which needs no any lattice trapdoor and is proved secure in the standard model (assuming interaction during the proof phase), outweighing the existing post-quantum secure GE systems in terms of functions, efficiency and security.
Funding
National Natural Science Foundation of China (2019B030302008)
History
Journal title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)