arXiv subjects

Ron D. Rothblum

Publications and source records attributed to Ron D. Rothblum.

3 recordsLinked to original sources

arXiv2026

Flock: Fast Proving for Batch Boolean Computations

For many applications of SNARKs, a key bottleneck is proving large batches of standard cryptographic hash evaluations, such as SHA-256, Keccak, or BLAKE3. We introduce Flock, a hash-based SNARK for extremely fast proving of such batched Boolean computations. Flock proves batches of the same R1CS circuit (plus input/output relations between them), can prove hash-chains and Merkle path openings, and in principle can be extended to full-fledged hash-based signature verification. At its core, Flock combines new optimizations for the lincheck and zerocheck protocols with an aggressively optimized proof-of-concept implementation co-designed by coding agents. On a single core of an M4 Max processor, Flock proves 82k evaluations of the BLAKE3 compression function, 42k SHA-256 compressions, and 30k Keccak permutations per second --- less than a $250\times$ overhead over native execution. On ten cores, throughput exceeds 660k BLAKE3 compressions per second; in proving SHA-256, Flock is more than $9\times$ faster than Binius64, the prior state of the art, and more than $500\times$ faster than the fastest elliptic curve-based SNARK we measured against.

cs.CR↗

arXiv2024

Distribution-Free Proofs of Proximity

Motivated by the fact that input distributions are often unknown in advance, distribution-free property testing considers a setting where the algorithmic task is to accept functions $f : [n] \to \{0,1\}$ with a certain property P and reject functions that are $η$-far from P, where the distance is measured according to an arbitrary and unknown input distribution $D \sim [n]$. As usual in property testing, the tester can only make a sublinear number of input queries, but as the distribution is unknown, we also allow a sublinear number of samples from the distribution D. In this work we initiate the study of distribution-free interactive proofs of proximity (df-IPPs) in which the distribution-free testing algorithm is assisted by an all powerful but untrusted prover. Our main result is that for any problem P $\in$ NC, any proximity parameter $η> 0$, and any (trade-off) parameter $t\leq\sqrt{n}$, we construct a df-IPP for P with respect to $η$, that has query and sample complexities $t+O(1/η)$, and communication complexity $\tilde{O}(n/t + 1/η)$. For t as above and sufficiently large $η$ (namely, when $η> t/n$), this result matches the parameters of the best-known general purpose IPPs in the standard uniform setting. Moreover, for such t, its parameters are optimal up to poly-logarithmic factors under reasonable cryptographic assumptions for the same regime of $η$ as the uniform setting, i.e., when $η\geq 1/t$. For small $η$ (i.e., $η< t/n$), our protocol has communication complexity $Ω(1/η)$, which is worse than the $\tilde{O}(n/t)$ communication complexity of the uniform IPPs (with the same query complexity). To improve on this gap, we show that for IPPs over specialised, but large distribution families, such as sufficiently smooth distributions and product distributions, the communication complexity reduces to $\tilde{O}(n/t^{1-o(1)})$.

cs.CC↗

arXiv2019

Hard properties with (very) short PCPPs and their applications

We show that there exist properties that are maximally hard for testing, while still admitting PCPPs with a proof size very close to linear. Specifically, for every fixed $\ell$, we construct a property $\mathcal{P}^{(\ell)}\subseteq\{0,1\}^n$ satisfying the following: Any testing algorithm for $\mathcal{P}^{(\ell)}$ requires $Ω(n)$ many queries, and yet $\mathcal{P}^{(\ell)}$ has a constant query PCPP whose proof size is $O(n\cdot \log^{(\ell)}n)$, where $\log^{(\ell)}$ denotes the $\ell$ times iterated log function (e.g., $\log^{(2)}n = \log \log n$). The best previously known upper bound on the PCPP proof size for a maximally hard to test property was $O(n \cdot \mathrm{poly}\log{n})$. As an immediate application, we obtain stronger separations between the standard testing model and both the tolerant testing model and the erasure-resilient testing model: for every fixed $\ell$, we construct a property that has a constant-query tester, but requires $Ω(n/\log^{(\ell)}(n))$ queries for every tolerant or erasure-resilient tester.

cs.CC↗