Modular Sumcheck Proofs with Applications to Machine Learning and Image Processing

ePrint Report: Modular Sumcheck Proofs with Applications to Machine Learning and Image Processing

David Balbás, Dario Fiore, Maria Isabel González Vasco, Damien Robissout, Claudio Soriente

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 …

applications cryptographic data data processing eprint report fairness general image integrity machine machine learning modular privacy proof purpose report scale systems