On September 13, the privacy protocol Nillion announced on the X platform that it would introduce blind computation and blind storage to the L1 public chain NEAR, which focuses on speed and scalability. This integration combines NEAR's performance with Nillion's advanced privacy tools. After that, more than 750 projects in the NEAR ecosystem can use blind computation.
NEAR and Nillion Integration: The Intersection of Privacy and Performance
As an established L1 blockchain network, NEAR has always been known for its performance. Its three main feature packages are:
Nightshade Sharding: NEAR’s unique sharding solution increases transaction throughput and reduces latency, making it ideal for high-performance applications.
WebAssembly Runtime: NEAR’s Wasm-based virtual machine supports smart contracts in Rust and AssemblyScript, attracting developers from a variety of backgrounds.
Human-readable accounts: NEAR uses intuitive account names, improving user experience and accessibility.
These features have attracted developers, entrepreneurs, and creatives, who have together built a thriving ecosystem of more than 750 apps.
By combining Nillion’s blind computation capabilities with NEAR’s efficient transaction processing, we achieve:
Modular Data Privacy: Nillion’s privacy features integrate smoothly with NEAR, allowing modular execution of data storage and computing operations in the Nillion network while transparently settling on the NEAR blockchain. This modularity provides developers with flexibility when designing their application architecture.
Private Data Management: Nillion extends 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 limitations and attract privacy-conscious users.
Private AI: Near’s focus on autonomous, user-owned AI complements Nillion’s private storage and compute capabilities to open up a vast new design space for decentralized AI.
Expanding the encryption project build space
This integration opens up new avenues for privacy-preserving applications within the NEAR ecosystem, with a particular focus on AI solutions:
Private AI:
Private Inference: Nillion enables secure inference of AI models, providing protection for proprietary machine learning (ML) models and users who provide sensitive inputs to them, initially focusing on private models such as regression, time series prediction, or classification.
Private Agents: With the rise of AI agents acting in a (semi-)autonomous manner, the need for privacy solutions becomes critical. Support for intent classification allows users to use agents without revealing information about their original query or the actions taken by the agent based on said query.
Federated Learning: While federated learning primarily focuses on training models on decentralized datasets without centralizing the data, Nillion can enhance privacy by protecting the aggregation process, ensuring that sensitive information derived during training (such as gradients) remains confidential.
Private synthetic data: Nillion can be a solution to protect the privacy of the underlying data during GAN training. Applying MPC to the training of GANs ensures that the data used in the training process is never exposed to other participants.
Private Retrieval-Enhanced Generation (RAG): Nillion enables a novel privacy-preserving approach to information retrieval, facilitating quantum-secure storage of vectors at rest and semantic search evaluation without decryption.
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.
Privacy-first community platform:
Decentralized communities can 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 can also facilitate blind voting, private proposal submission, and secure fund management.
Secure DeFi:
Nillion’s blind computation enables private order books, confidential loan assessments, and hidden liquidity pools, enhancing the security and privacy of NEAR’s growing DeFi ecosystem.
Developer tools to protect privacy:
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.
The Future of Blind Computation on NEAR
By combining NEAR’s high-performance infrastructure with Nillion’s advanced privacy features, we are creating an environment where developers can build powerful, privacy-preserving applications that meet real-world needs. This will help create a new open digital economy where people control their own assets and data.
