mlsanityData to 14 Oct 2026

opp/ai: Optimistic Privacy-Preserving AI on Blockchain

arXiv 2402.15006 ↗First posted 22 Feb 2024cs.CR cs.LGcited by 1

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Abstract (arXiv)

The convergence of Artificial Intelligence (AI) and blockchain technology is reshaping the digital world, offering decentralized, secure, and efficient AI services on blockchain platforms. Despite the promise, the high computational demands of AI on blockchain raise significant privacy and efficiency concerns. The Optimistic Privacy-Preserving AI (opp/ai) framework is introduced as a pioneering solution to these issues, striking a balance between privacy protection and computational efficiency. The framework integrates Zero-Knowledge Machine Learning (zkML) for privacy with Optimistic Machine Learning (opML) for efficiency, creating a hybrid model tailored for blockchain AI services. This study presents the opp/ai framework, delves into the privacy features of zkML, and assesses the framework's performance and adaptability across different scenarios.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 27 references that are in the map. First posted on arXiv 22 Feb 2024.

Builds on

9 of 27 references.

References · 9

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  3. 2017VGGFace2: A dataset for recognising faces across pose and agecited by 914
  4. 2016Stealing Machine Learning Models via Prediction APIscited by 619
  5. 2017Distributed Deep Neural Networks over the Cloud, the Edge and End Devicescited by 120
  6. 2018Edge-Host Partitioning of Deep Neural Networks with Feature Space Encoding for Resource-Constrained Internet-of-Things Platformscited by 35
  7. 2024opML: Optimistic Machine Learning on Blockchaincited by 21
  8. 2021Agatha: Smart Contract for DNN Computationcited by 6
  9. 2022Dispute-free Scalable Open Vote Network using zk-SNARKscited by 1

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Citing papers per year: 2026: 1.

1 paper.

  1. 2026Hawkeye: Reproducing GPU-Level Non-Determinismcited by 2