(2026-04) Non-Adaptive Programmable PRFs and Applications to Stacked Garbling
2026-04-25
Garbled circuits are a fundamental primitive in cryptography. While the size of garbled circuits in Yao's original scheme grows linearly with the circuit size, a recent line of work on stacked garbling (SGC) [Heath-Kolesnikov, CRYPTO'20] has achieved near-sublinear size for branching computations, based only on one-way functions. Specifically, these schemes achieve garbled size growing only with the size of a single branch and the total input length to all the branches. Due to the latter dependence, these results are best suited to "small" input settings.
We present a stacked garbling scheme for "large" input settings based on one-way functions. The garbled size in our scheme grows only with the size of a single branch and its input length (up to logarithmic factors), plus an additive term in the number of branches (as in prior SGC).
To obtain our result, we uncover a connection between stacked garbling and the notion of (adaptive) programmable pseudorandom functions (apPRFs) [Boneh-Lewi-Wu, PKC'17]. While existing apPRF constructions either rely on stronger assumptions (e.g., learning with errors or indistinguishability obfuscation) or incur noticeable security losses under weaker assumptions, we identify a relaxed notion of non-adaptive programmable PRFs (napPRFs) that suffices for our result, and establish its feasibility based on one-way functions. Interestingly, we build on techniques from the SGC literature to construct napPRFs with our desired efficiency, and then apply napPRFs back to SGC to obtain our main result.
Along the way, as an additional result of independent interest, we provide the first construction of (adaptive) programmable PRFs for polynomial-size domains based on one-way functions.