Paper 2026/2298 Original: eprint.iacr.org/2026/2298 ↗

Pseudorandom Correlation Generators for Matrix Triples

Abstract

We introduce new pseudorandom correlation generators (PCGs) for matrix triples: shares of triples $(A,B,A\cdot B)$ where $A,B$ are random matrices over $\mathbb{F}^{n\times n}$ for a field $\mathbb{F}$ and an integer $n$. Matrix triples are consumed in a wide variety of secure computation protocols that involve matrix arithmetic, which occurs commonly in settings such as privacy-preserving machine-learning and statistics, threshold cryptography, secure linear algebra, and more. Previous PCGs for matrix triples are inefficient: their seed grows at least linearly in the matrix dimension, and their expansion is slow. In this work, we introduce two new PCGs with improved performances: 1. Our first construction, inspired by the recent work of (Gentry and Lee, Crypto 2026) on FHE for matrix arithmetic, relies on the quasi-abelian syndrome decoding (QASD) assumption introduced in (Bombar et al., Crypto 2023), an assumption which has since received a significant amount of attention. Our PCG generates $N$ matrix triples over $\mathbb{F}^{n\times n}$ from a seed of size $poly(\lambda,\log n,\log N)$ using $\Theta(n^3\log(n^3N))$ field operations per triple, removing the linear dependency on $n$ from the seed compared to previous works. 2. Our second construction is more mathematically involved, and builds upon an elegant and surprising connection with the Weyl-Pauli twisted algebra, a mathematical structure originally studied in (Weyl, Zeitschrift für Physik, 1927) in the context of quantum kinematics. Our PCG relies on a new variant of the QASD assumption over the Weyl-Pauli twisted group algebra and exhibits impressive performances: it has the same seed size as our first construction and generates $N$ matrix triples at a cost only a small constant time larger than the cost of computing $N$ matrix triples in the clear. We complement our results with an in-depth study of our new twisted QASD assumption and demonstrate the benefits of our PCGs in concrete applications such as privacy-preserving machine learning and threshold MAYO.