# 0x00 Limitations of Traditional Data Processing
In today's digital age, data has become an extremely valuable asset, especially in scenarios involving sensitive information, where data security and privacy are crucial.
However, when sensitive data is encrypted and stored, **traditional methods** often require decryption of data to perform useful work, followed by re-encrypting the results. This 'decrypt - compute - re-encrypt' cycle not only exposes the data to the operators of the computational infrastructure during processing, increasing the risk of data breaches, but also brings many disadvantages and challenges.
# 0x01 Nillion's Solutions and Principles
Nillion utilizes various privacy-enhancing technologies (PETs) such as multi-party computation (MPC), homomorphic encryption (HE), and trusted execution environments (TEEs) to facilitate secure data computation and storage on a decentralized infrastructure. This means data will not be decrypted throughout its entire lifecycle of transmission, storage, and processing, greatly reducing the risk of data breaches. Whether for training AI models, processing financial transactions, or analyzing medical data, Nillion can provide users with strong privacy protection.
Data is stored, transmitted, and computed while always remaining encrypted, allowing tasks to be completed without decryption, thereby ensuring data privacy and security. This process mainly relies on privacy-enhancing technologies such as multi-party computation (MPC) and fully homomorphic encryption (FHE), as well as its dual-layer architectural design: the coordination layer (nilChain) is responsible for task scheduling and payment settlement, while the Petnet layer is responsible for executing secure computations and managing encrypted data.
Data Encryption: Users encrypt sensitive data before uploading it to the Nillion network. This data remains encrypted throughout its lifecycle and is never decrypted.
Task Allocation: Users submit computation task requests in Nillion's coordination layer (nilChain), including the computation operations to be performed and related parameters.
Computation Execution: The coordination layer allocates tasks to validator nodes in the Petnet layer, which use multi-party computation (MPC) and fully homomorphic encryption (FHE) technologies to compute directly on encrypted data without decryption.
Result Verification: After computation, the validator nodes verify the correctness of the results using techniques such as zero-knowledge proofs (ZKP) to ensure the credibility of the results.
Result Return: The verified computation results are sent back to the coordination layer and ultimately returned to the user. Users can decrypt the encrypted results to obtain the final plaintext results, while data remains undisclosed throughout the process.
Through this sequence of operations, Nillion achieves efficient and secure computation processing while protecting data privacy.
# 0x02 Nillion's Application Scenarios
To facilitate developers in utilizing Nillion's blind computing capabilities, the project provides a series of application tools such as nilAI, nilVM, nilDB, and nilChain. These tools cover multiple fields including artificial intelligence, healthcare, and decentralized finance, providing developers with rich resources to build privacy-preserving applications.
The following are its main tracks:
Artificial Intelligence (AI) and Machine Learning: Nillion's blind computing technology supports the private training and inference of AI models, ensuring that data remains encrypted throughout the processing, thus providing strong privacy protection for applications such as personalized AI services, private knowledge bases, and retrieval-augmented generation (RAG). This not only meets the demands of AI for data privacy but also promotes the development of privacy-preserving AI applications.
Decentralized Finance (DeFi): In the DeFi space, Nillion's privacy features can protect users' data and asset security, supporting identity compliance, financial innovation, and personalized financial services. Nillion, by supporting cross-chain functionality, can integrate with multiple blockchain networks (such as Near, Aptos, Arbitrum, etc.), expanding the application scope of its ecosystem.
Other privacy scenarios, such as healthcare (DeSci): Nillion's technology allows medical institutions and researchers to conduct data analysis and research without disclosing patient privacy, providing strong support for the secure management and analysis of medical data.
Nillion provides a new solution for data privacy protection through its innovative blind computing technology, breaking through the limitations of traditional data processing models. It not only meets the stringent requirements for data privacy in fields such as artificial intelligence, decentralized finance, and healthcare, but also provides powerful tools for developers to build privacy-preserving applications, promoting innovation and application of privacy computing technology in more fields.
