Welcome to the resource topic for 2025/1528
Title:
Trustless Delegation of Vector Commitment Construction in Resource-Constrained Settings
Authors: Parisa Hassanizadeh, Shahriar Ebrahimi, Stefan Dziembowski, Janusz Szczepanski
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 volume constraints, as in surveillance, journalism, and public records. Vector commitment (VC) schemes that support verifiable partial disclosures address this need, but constructing and maintaining such VCs is challenging for resource-constrained devices such as cameras. For example, in CCTV systems, a trusted module updating the VC for millions of frames must store and process hundreds of megabytes, which is often infeasible.
We propose a proving system that enables trustless reconstruction of VCs from only the chained hash of the sequence. The data source computes and signs a cumulative hash. An untrusted prover then constructs the VC from the raw data and proves that the data used for construction is consistent with the signed hash chain. The constructed VC subsequently enables authentication of partial disclosures. The main challenge is that proving the full VC construction in zero-knowledge is computationally expensive. To address this, we design a folding-based zkSNARK tailored to this task.
We analyze the construction in a realistic setting with a constrained source device (Raspberry Pi Zero) and a consumer-grade prover (midrange laptop). Our results show that even for relatively short sequences (e.g., 30 minutes of video footage), handling the VC within the source device’s trusted module is infeasible due to both storage and computational limitations. In contrast, the proposed trustless offloading imposes only a few minutes of local computation on the prover to generate a proof for full VC construction (around 2 minutes for the 30 minutes of footage). Our implementation is available as open source at: GitHub - zero-savvy/proven-view: Verifiable video trimming/editing software using Folding-based zkSNARKs.
ePrint: https://eprint.iacr.org/2025/1528
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