Sublinear-Space Zero-Knowledge Proving for Efficient On-Device Verification ∞ Research

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Briefing

Modern zero-knowledge proof systems face a critical limitation where prover memory scales linearly with computation trace length, impeding their use on resource-constrained devices and for large-scale tasks. This paper presents the first sublinear-space ZKP prover, achieving this by reframing proof generation as an instance of the classic Tree Evaluation problem. This foundational breakthrough enables a transformative shift from specialized, server-bound proving to efficient on-device proving, profoundly impacting the future of decentralized systems, on-device machine learning, and privacy-preserving technologies.

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Context

Prior to this research, a significant limitation in zero-knowledge proof systems was the prover’s memory consumption, which scaled linearly with the length of the computation’s execution trace. This inherent demand for substantial memory rendered ZKPs impractical for deployment on resource-constrained devices and economically unfeasible for computations involving extensive trace lengths, thereby restricting their widespread adoption despite their theoretical benefits for privacy and verifiable computation.

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Analysis

The paper’s core innovation is the construction of a sublinear-space ZKP prover. This new primitive fundamentally redefines proof generation by establishing an equivalence to the classic Tree Evaluation problem. Leveraging a recently developed space-efficient algorithm for tree evaluation, the system designs a streaming prover. This streaming approach allows the prover to assemble the zero-knowledge proof without ever fully materializing the entire execution trace, a significant departure from previous methods that required linear memory.

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Parameters

Core Concept ∞ Sublinear-Space Zero-Knowledge Prover
Key Reduction ∞ Tree Evaluation Problem
Memory Reduction ∞ O(sqrt(T))
Key Author ∞ Logan Nye
Publication Date ∞ August 30, 2025

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Outlook

This breakthrough in prover memory efficiency opens new research avenues in optimizing cryptographic primitives for ubiquitous computing environments. The ability to perform on-device proving unlocks potential real-world applications in the next 3-5 years, including privacy-preserving machine learning on mobile devices, decentralized identity solutions, and highly scalable verifiable computation for edge devices. Further research will likely explore reducing the O(sqrt(T)) memory footprint and integrating this streaming prover into existing ZKP frameworks to maximize practical impact.

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Verdict

This research fundamentally redefines the practicality of zero-knowledge proofs by enabling efficient on-device proving, critically advancing the foundational principles of verifiable computation and privacy.

Signal Acquired from ∞ arXiv.org