Pankaj Dayama, Vinayaka Pandit, et al.
CCS 2024
We propose the first distributed version of a simple, efficient, and provably quantum-safe pseudorandom function (PRF). The distributed PRF (DPRF) supports arbitrary threshold access structures based on the hardness of the well-studied Learning with Rounding (LWR) problem. Our construction (abbreviated as PQDPRF) practically outperforms not only existing constructions of DPRF based on lattice-based assumptions, but also outperforms (in terms of evaluation time) existing constructions of: (i) classically secure DPRFs based on discrete-log hard groups, and (ii) quantum-safe DPRFs based on any generic quantum-safe PRF (e.g. AES). The efficiency of PQDPRFstems from the extreme simplicity of its construction, consisting of a simple inner product computation over , followed by a rounding to a smaller modulus . The key technical novelty of our proposal lies in our proof technique, where we prove the correctness and post-quantum security of PQDPRF (against semi-honest corruptions of any less than threshold number of parties) for a polynomial q/p (equivalently, “modulus to modulus”)-ratio. Our proposed DPRF construction immediately enables efficient yet quantum-safe instantiations of several practical applications, including key distribution centers, distributed coin tossing, long-term encryption of information, etc. We showcase a particular application of PQDPRF in realizing an efficient yet quantum-safe version of distributed symmetric-key encryption ( DiSE– originally proposed by Agrawal et al. in CCS 2018), which we call PQ - DiSE. For semi-honest adversarial corruptions across a wide variety of corruption thresholds, PQ – DiSE substantially outperforms existing instantiations of DiSE based on discrete-log hard groups and generic PRFs (e.g. AES). We illustrate the practical efficiency of our PQDPRF via prototype implementation of PQ – DiSE.
Pankaj Dayama, Vinayaka Pandit, et al.
CCS 2024
Zhiyuan He, Yijun Yang, et al.
ICML 2024
Teryl Taylor, Frederico Araujo, et al.
Big Data 2020
Anisa Halimi, Leonard Dervishi, et al.
PETS 2022