RT Conference Proceedings
T1 Modular Sumcheck Proofs With Applications to Machine Learning and Image Processing
A1 Balbás, David
A1 Fiore, Dario
A1 González Vasco, María Isabel
A1 Robissout, Damien
A1 Soriente, Claudio
AB Cryptographic proof systems provide integrity, fairness, and privacy in applications that outsource data processing tasks. However, general-purpose proof systems do not scale well to large inputs. At the same time, ad-hoc solutions for concrete applications - e.g., machine learning or image processing - are more efficient but lack modularity, hence they are hard to extend or to compose with other tools of a data-processing pipeline. In this paper, we combine the performance of tailored solutions with the versatility of general-purpose proof systems. We do so by introducing a modular framework for verifiable computation of sequential operations. The main tool of our framework is a new information-theoretic primitive called Verifiable Evaluation Scheme on Fingerprinted Data (VE) that captures the properties of diverse sumcheck-based interactive proofs, including the well-established GKR protocol. Thus, we show how to compose VEs for specific functions to obtain verifiability of a data-processing pipeline. We propose a novel VE for convolution operations that can handle multiple input-output channels and batching, and we use it in our framework to build proofs for (convolutional) neural networks and image processing. We realize a prototype implementation of our proof systems, and show that we achieve up to 5x faster proving time and 10x shorter proofs compared to the state-of-the-art, in addition to asymptotic improvements.
PB Association for Computing Machinery (ACM)
SN 979-8-4007-0050-7
YR 2023
FD 2023-11-21
LK https://hdl.handle.net/10016/39111
UL https://hdl.handle.net/10016/39111
LA eng
DS e-Archivo
RD 17 jul. 2024