Paper 2025/2336 Original: eprint.iacr.org/2025/2336 ↗

Compact Adaptively Secure Identity-Based Encryption from Middle-Product Learning with Errors

Abstract

Identity-Based Encryption (IBE) is a cryptographic primitive where any string, such as an email address, can serve as a public key. With the advent of quantum computing, post-quantum secure IBE constructions have become critical for ensuring long-term data security. The state-of-the-art construction based on MPLWE introduced by Fan et al. significantly advanced the field by achieving adaptive security under standard assumptions, however the size of the master public key (MPK) grows linearly with the identity length, posing scalability challenges for real-world applications. In this work, we build on Fan et al.'s construction by employing a fully homomorphic trapdoor function to optimize the number of polynomials required for generating secret keys. This approach significantly reduces the MPK size from $O(\ell)$ polynomial vectors to $O(\ell^{1/d})$, where $d$ is a constant. Despite this compactness, our scheme retains the same secret key and ciphertext sizes as Fan et al.'s construction and introduces no additional security assumptions.