# opp/ai

> oppai is a proving framework that combines efficiency of opML with privacy aspects of zkML.

- Canonical URL: https://iq.wiki/wiki/oppai
- Categories: Projects & Protocols
- Tags: Layer 1, AI Token, Developer
- Created: 2024-06-27T16:54:49.332Z
- Last updated: 2026-04-14T15:14:49.000Z
- Source: IQ.wiki — the world's largest blockchain and crypto encyclopedia (https://iq.wiki)

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# Introduction

opp/ai (Optimistic Privacy-Preserving AI), invented by ORA, is an onchain AI framework designed to address the challenges of privacy and computational efficiency in blockchain-based machine learning systems. [\[1\]](#cite-id-s6pxyJRT718Shmeu) It integrates the privacy guarantees of Zero-Knowledge Machine Learning (zkML) with the efficiency of Optimistic Machine Learning ([opML](https://iq.wiki/wiki/opml)), creating a hybrid model for secure AI services onchain. [\[2\]](#cite-id-x7yOZCAV6FgiVrIh) The framework is designed to be flexible, meaning that advances in the underlying zkML technology can be directly incorporated into opp/ai.

# Technology

The opp/ai framework is a hybrid that combines two core technologies to balance privacy with efficiency.

### zkML (Zero-Knowledge Machine Learning)

Zero-Knowledge Machine Learning (zkML) is used in the opp/ai framework to provide privacy. It leverages zero-knowledge proofs (ZKPs) to verify computations without revealing the underlying sensitive data or model parameters. [\[1\]](#cite-id-s6pxyJRT718Shmeu) This allows for the protection of confidential information during AI inference. However, generating ZKPs is computationally intensive and can be costly, which is a primary reason for opp/ai's hybrid approach. [\[2\]](#cite-id-x7yOZCAV6FgiVrIh)

### opML (Optimistic Machine Learning)

Optimistic Machine Learning ([opML](https://iq.wiki/wiki/opml)) is used to ensure computational efficiency. Unlike zkML, [opML](https://iq.wiki/wiki/opml) uses a fraud-proof system where ML results are executed off-chain and submitted to the [blockchain](https://iq.wiki/wiki/blockchain) with an optimistic assumption of their correctness. [\[1\]](#cite-id-s6pxyJRT718Shmeu) These results are subject to a challenge period during which they can be disputed. This method significantly reduces the onchain computational load, offering a more scalable and efficient solution for integrating ML with [blockchain](https://iq.wiki/wiki/blockchain) technology. [\[2\]](#cite-id-x7yOZCAV6FgiVrIh)

# How it Works

The opp/ai framework operates by strategically partitioning a machine learning model into different submodels based on its privacy requirements. This creates a hybrid execution model that balances privacy and efficiency. [\[2\]](#cite-id-x7yOZCAV6FgiVrIh)​

* **zkML Submodels:** Components that handle proprietary algorithms or sensitive data are designated as zkML submodels. These are executed using Zero-Knowledge Proofs to maintain the confidentiality of the model weights or data.
* **opML Submodels:** Components where efficiency is prioritized over privacy are designated as [opML](https://iq.wiki/wiki/opml) submodels. These are executed off-chain using the optimistic approach.

The outputs from the zkML submodels can serve as inputs for the [opML](https://iq.wiki/wiki/opml) submodels, allowing for a seamless integration of the two systems. For the [opML](https://iq.wiki/wiki/opml) components, disputes are resolved through an interactive game that utilizes a Fraud Proof Virtual Machine (FPVM) to verify computation steps onchain. [\[1\]](#cite-id-s6pxyJRT718Shmeu)​

A primary application of this is concealing specific fine-tuning weights of a model where the majority of the weights are already public. For example, the proprietary LoRA weights in the attention layers of an open-source model like Stable Diffusion can be protected using the opp/ai framework. This preserves the competitive advantage of the unique adaptations while the base model remains accessible. [\[1\]](#cite-id-s6pxyJRT718Shmeu)​

# Use Cases

* **Proprietary Fine-Tuned Models:** opp/ai can conceal fine-tuning weights for models where most weights are public, such as protecting custom LoRA weights in open-source generative AI models. This is crucial for preserving proprietary enhancements made to publicly shared models. [\[1\]](#cite-id-s6pxyJRT718Shmeu)
* **Individual Voice Tuning:** Text-to-voice service providers can use opp/ai to protect personalized voice models tailored to an individual's sensitive voice data. The framework ensures the model's parameters remain confidential while the service is verifiably offered to users.
* **Financial Sector:** Trading algorithms, which are highly valuable and contain sensitive strategies, can be protected. A financial firm could use opp/ai to conceal the proprietary weights of a model specifically tuned to its trading strategy, allowing for secure and verifiable onchain trading operations without exposing trade secrets.
* **Gaming Industry:** AI models used for non-player characters (NPCs) can be fine-tuned to create unique and engaging behaviors. Developers can use opp/ai to hide the fine-tuned weights, protecting their competitive edge and preventing others from easily copying unique NPC strategies.