Nillion

Nillion decentralizes trust for sensitive data in the same way that blockchains decentralized transactions.

We're in the midst of a data renaissance. Emerging ecosystems like personalized AI, decentralized trading and identity risk the safety, power and control of important data by entrusting it to one entity.

The NEAR-Nillion Integration

By combining Nillion’s Blind Computation capabilities with NEAR’s efficient transaction processing, we unlock:

• Modular Data Privacy: Nillion’s privacy features integrate smoothly with NEAR, allowing for modular execution of data storage and compute operations in the Nillion Network alongside transparent settlement on the NEAR blockchain. This modularity offers developers flexibility in designing their applications’ architecture.
• Private Data Management: Nillion expands NEAR’s capabilities by providing private storage and computation for all types of data. This significantly broadens the design space for privacy-preserving applications in the NEAR ecosystem, enabling developers to build solutions that were previously impossible due to privacy constraints and attracting privacy-conscious users.
• Private AI: Near’s focus on self-sovereign, user-owned AI is complemented by Nillion’s private storage and computing capabilities, unlocking vast new design space for decentralized AI.

Expanding the Design Space

This integration opens up exciting new avenues for privacy-preserving applications within the NEAR ecosystem, with a particular focus on AI solutions:

1. Private AI:
   • Private inference: Nillion could enable secure inference of AI models that offer protection for both proprietary machine learning (ML) models and users providing sensitive inputs to them, with an initial focus on private models like regression, time-series forecasting, or classification.
   • Private agents: With the rise of AI agents taking actions in a (semi-)autonomous manner, the need for privacy solutions becomes critical. The support for intent classification could enable users to use agents without leaking information about their original query or what action the agent took based on said query.
   • Federated Learning: Although federated learning primarily focuses on training models across decentralized datasets without centralizing data, Nillion could enhance privacy by securing the aggregation process, ensuring that sensitive information derived during training (like gradients) remains confidential.
   • Private synthetic data: Nillion could emerge as a solution to protect the underlying data privacy during the GAN training process. Applying MPC to the training of GANs ensures that the data used in the training process is never exposed to other participants.
   • Private Retrieval Augmented Generation (RAG): Nillion could enable a novel privacy-preserving approach for information retrieval, facilitating quantum-secure storage of vectors at rest and semantic search evaluation without decryption.
2. Cross-Chain Privacy Solutions: Given NEAR’s emphasis on interoperability, this integration could pave the way for privacy-preserving cross-chain applications and asset transfers.
3. Privacy-First Community Platforms: Decentralized communities could benefit from content and social graphs stored privately in Nillion and processed to recommend targeted, personalized content, combining the benefits of decentralization with privacy. The platform could also facilitate blind voting, private proposal submissions, and secure treasury management.
4. Secure DeFi: Nillion’s Blind Computation could enable private order books, confidential lending assessments, and hidden liquidity pools, enhancing the security and privacy of NEAR’s growing DeFi ecosystem.
5. Privacy-Preserving Developer Tools: Nillion’s Blind Computation can enhance NEAR’s developer-friendly environment by providing privacy-focused tools and APIs, allowing developers to easily incorporate advanced privacy features into their applications without sacrificing NEAR’s ease of use and scalability.

Recently, NEAR Founder Illia Polosukhin appeared on the Blockworks podcast to discuss NEAR’s integration with Nillion. Check out the conversation below:

The Future of Blind Compute on NEAR

By combining NEAR’s high-performance infrastructure with Nillion’s advanced privacy capabilities, we are creating an environment where developers can build powerful, privacy-preserving applications that scale to meet real-world demands.

We’re excited to join NEAR on their mission to create a new open digital economy where people control their assets and data. For more information about NEAR Protocol, visit their website or follow them on Twitter.

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