Paper 2026/410

Collaborative Incrementally Verifiable Computation

Abstract

Collaborative zkSNARKs allow multiple mutually distrustful parties to jointly prove the correctness of a computation without revealing their private inputs. This enables a new class of exciting secure applications, such as privacy-preserving healthcare data aggregation, privacy preserving audits, and jointly trained machine learning models. Unfortunately, existing collaborative zkSNARKs still struggle to support many target applications in practice, which operate over large-scale datasets. This is due to prohibitive memory and communication overheads, both of which may be orders of magnitude larger than the original datasets, as well as the lack of updatability: If a dataset is updated, then the computation must be proved again from scratch. As any one of these limitations can be a bottleneck, we address them simultaneously with collaborative incrementally verifiable computation, which enables multiple mutually distrustful parties, each with its own private inputs, to jointly update a running succinct proof alongside each step of a streaming computation. For each step of computation, our construction features only constant communication overhead per party (assuming a broadcast channel), and memory overhead that only scales with the memory costs of a single step.