Parisa Hassanizadeh
IPPT Polish Academy of Science (PAN), Poland; Zero Savvy, United Kingdom

Shahriar Ebrahimi
Zero Savvy, United Kingdom; The Alan Turing Institute, United Kingdom

Stefan Dziembowski
University of Warsaw, Poland; IDEAS Institute, Warsaw, Poland

Janusz Szczepanski
IPPT Polish Academy of Science (PAN), Poland

Keywords: zkSNARKs, Provenance, Vector Commitments, Folding Schemes

Abstract

Many data types, such as video and audio, consist of sequential elements where both integrity and order are essential for authenticity. In practice, verifiers often access only partial sequences due to privacy or bandwidth constraints, motivating the use of vector commitments (VCs) for verifiable partial disclosure. However, VC construction requires a trusted committer, typically within a secure module on the device, and maintaining such commitments is challenging for resource-constrained hardware. For example, in CCTV pipelines, a trusted module processing continuous streams must store and update large VC structures, leading to prohibitive memory and computational overhead. This work addresses this deployment bottleneck by introducing an efficient pipeline for verifiable VC construction that offloads computation from constrained devices while preserving trust. The source device computes and signs a cumulative hash over the data stream, requiring only constant memory. Later, an untrusted prover reconstructs the VC from the raw data and produces a zero-knowledge proof that the construction is consistent with the signed hash chain. This design eliminates the need for trusted storage of intermediate VC state and enables verifiable partial disclosures from the reconstructed VC. A key challenge is the high cost of proving the full VC construction. We address this by designing a folding-based zkSNARKs system tailored to streaming workloads. We implement and evaluate the system on a constrained device (Raspberry Pi Zero) as the source and a consumer-grade prover (midrange laptop). Our results show that direct VC maintenance on the source device requires hundreds of megabytes of memory and is computationally infeasible on trusted platform standards for even moderate workloads (e.g., 30 minutes of video). In contrast, our approach reduces the trusted device’s memory footprint to constant size, while the midrange laptop can generate the proof of full VC construction in approximately 2 minutes for the same workload. Furthermore, the proof size is around 10 KB regardless of the original size of data and verification time is sub-second. Our implementation is available open source at: https://github.com/zero-savvy/proven-view.