1. Introduction

1.1 Background and Purpose

With the continuous development of blockchain technology, the cryptocurrency market has shown vigorous development, attracting widespread attention from global investors. From the birth of early Bitcoin, which opened the era of decentralized digital currency, to the current diverse cryptocurrencies such as Ethereum, the cryptocurrency market continues to expand in scale, with increasingly rich application scenarios. However, the cryptocurrency market is highly volatile and full of innovation and change, with new projects and ideas constantly emerging.

As a newcomer in the field of cryptocurrency, Nillion seeks to stand out in this competitive market with its unique technology and ideas. It is committed to addressing some long-standing issues in the current cryptocurrency market, such as privacy protection, data security, and scalability, to provide users with more secure, efficient, and privacy-protecting cryptocurrency services. Its innovative ‘blind computation’ technology, combining a variety of advanced encryption technologies, aims to achieve efficient processing of data in an encrypted state, which is of significant practical significance in today’s increasingly privacy-conscious environment.

2. Overview of Nillion Project

2.1 Basic Introduction

Nillion was founded in 2022 and is an innovative project dedicated to addressing privacy protection and data security issues in the cryptocurrency field. It aims to build a decentralized network composed of multiple computers, providing users with unprecedented privacy protection solutions through unique ‘blind computing’ technology.

‘Blind computation’ is the core technology of Nillion, which is an integrated result of multiple advanced encryption technologies, including Multi-Party Computation (MPC), Homomorphic Encryption, and other privacy-enhancing technologies (PET). This technology allows the server (node) to perform computational tasks on encrypted data fragments, without disclosing the data content, thereby achieving the goal of privacy protection.

Compared with other cryptographic technologies, ‘blind computation’ has unique advantages. For example, zero-knowledge proofs (ZKP) require huge costs to generate proofs, suitable for off-chain storage + computation, on-chain verification only scenarios; trusted execution environment (TEE) relies on hardware manufacturers to perform calculations in isolated environments; fully homomorphic encryption (FHE) can perform calculations directly on encrypted data, but currently only supports specific operations. ‘Blind computation’ is a more general computing framework that may aggregate encryption technologies such as ZKP, TEE, FHE, etc., exploring an integrated engineering solution for privacy protection.

In the Nillion network, the participating computers are called nodes. These nodes have powerful capabilities to transmit, store, and process data, and they do not need to ‘see’ the data itself when performing tasks. The nodes blindly execute programs, ignoring input data or output results. Taking the example of nodes representing user-signed transactions, each node receives a transaction and a key called ‘shared,’ which does not contain any actual information. By running encryption protocols, nodes can collectively sign transactions without needing to reconstruct keys or access the user’s private key, demonstrating the security and privacy protection of ‘blind computation’ technology.

2.2 Team and Financing Situation

Nillion’s founding team lineup is luxurious, with members from diverse backgrounds covering multiple fields such as blockchain, finance, and law, laying a solid foundation for the project’s success. Chief Strategy Officer Andrew Masanto is a co-founder of Hedera Hashgraph and brings rich experience and deep technical background in the blockchain field, providing valuable industry resources and strategic insights to Nillion. Chief Business Officer Slava Rubin is the founder of the American crowdfunding website Indiegogo, possessing outstanding business operations and market development capabilities to effectively drive Nillion’s growth and partnerships in the market. General Counsel Lindsay Danas Cohen, a former Deputy General Counsel at Coinbase, has extensive experience in cryptocurrency legal compliance, ensuring Nillion’s steady development on a compliant path.

In terms of financing, Nillion has also achieved remarkable results. In December 2022, Nillion closed a $20 million funding round led by Distributed Global with participation from AU21, Big Brain Holdings, Chapter One, GSR, HashKey, OP Crypto, and SALT Fund, demonstrating the market’s recognition and confidence in the Nillion project. In 2024, Nillion will once again complete a $25 million funding round led by Hack VC, which further strengthens Nillion’s financial strength and provides strong support for its technology research and development, market expansion, and ecosystem construction. These funds will be used to promote the R&D and application of “blind computing” technology, expand its influence in the field of blockchain and AI, and accelerate the realization of its vision and goals.

3. Nillion Technology Principles and Innovations

3.1 Analysis of ‘Blind Computing’ Technology

3.1.1 Technical Definition and Composition

“Blind computation” is the core technological highlight of Nillion crypto, which is an innovative result integrating various advanced encryption technologies such as multi-party computation (MPC) and homomorphic encryption. Multi-party computation is a cryptographic technology that allows multiple participants to jointly calculate a target function without revealing their respective data to each other. For example, in a scenario of multi-party joint statistics of user consumption data, each participant holds their own user consumption records. Through multi-party computation technology, they can jointly calculate total consumption amounts, average consumption, and other statistical data without exposing their respective user consumption records to other parties. Homomorphic encryption is a special form of encryption that allows specific computational operations to be performed on the ciphertext, with the results being consistent with the results of performing the same computational operations on the plaintext and then encrypting it. This means that data can be processed in an encrypted state without the need for decryption, greatly enhancing the security of the data.

‘Blind computation’ cleverly combines the advantages of these technologies to build a unique privacy-preserving computing system. It allows the server (node) to perform computing tasks on encrypted data fragments, ensuring that data content is not leaked throughout the process, thereby achieving the privacy and security of data processing. This integration is not a simple technology stack, but through carefully designed algorithms and protocols, various technologies collaborate and complement each other, jointly supporting the implementation of ‘blind computation’. For example, in the data preprocessing stage, homomorphic encryption technology is used to encrypt data to ensure the security of data during transmission and storage; in the calculation stage, multi-party computation technology is used to achieve collaborative computing among multiple nodes, while ensuring that each node cannot access the data content of other nodes.

3.1.2 Workflow and Mechanism

In the Nillion network, the workflow of ‘blind computation’ demonstrates a high level of complexity and precision. When the network receives data transmission processing requests, it first goes through specific language compilation preprocessing in the Nada language. The Nada language is a programming language designed specifically for ‘blind computation,’ which can split the original data into multiple segments and encrypt each segment, keeping the data encrypted throughout the subsequent transmission and processing processes. This step is similar to splitting a confidential file into multiple parts and encrypting each part separately, so even if one part is leaked, it will not expose the content of the entire file.

The preprocessed data segments will enter the AIVM virtual machine for scheduling and allocation. The AIVM virtual machine is like an intelligent resource manager, which will randomly distribute the data segments to distributed nodes for storage and computation based on factors such as the load and computing power of each node. After receiving the data segments, each node will process them in its own environment. Since the data is encrypted, the nodes do not know the specific content of the data during the computation process and can only operate on the encrypted data according to predefined algorithms. It’s like a node processing a sealed package, not knowing what’s inside, but being able to perform specific operations on the package as required.

When each node completes the computation, it will return the calculation results. These results will undergo aggregation and unified verification to ensure the accuracy and integrity of the computation. During this process, some verification algorithms and technologies may be used, such as zero-knowledge proofs, to verify the correctness of the computation results while not revealing the content of the data. Through this process, the Nillion network achieves encrypted transmission, storage, and computation of data effectively without nodes knowing the “complete” data, fully demonstrating the security and privacy protection of the ‘blind computation’ technology.

3.2 compared to other encryption technologies

Compared with zero-knowledge proof (ZKP), ZKP mainly focuses on verifying the authenticity of information without revealing any specific content. In the transfer scenario of anonymous currency like Zcash, when users make transfers, they need to generate a ZK proof to prove their right to transfer and ensure the anonymity of their identity. However, generating ZKP proof requires huge expenses, making it more suitable for off-chain storage + computation and on-chain verification-only scenarios, such as Rollup Layer2. On the other hand, ‘blind computation’ not only focuses on information verification, but also emphasizes the encryption and computation of data throughout the entire processing, making it a more comprehensive privacy protection solution without the need to generate such huge expenses during the computation process.

Trusted Execution Environment (TEE) is a method that relies on hardware manufacturers to perform calculations in isolated environments. It executes computing tasks in a relatively closed environment using the security mechanisms provided by hardware to ensure data security. However, the application of TEE is limited by hardware, as different hardware manufacturers may provide different TEE solutions, and the cost and compatibility of hardware are also factors to consider. On the other hand, “blind computation” is based on software-level integration of encryption technology, independent of specific hardware, and offers better generality and scalability.

Fully homomorphic encryption (FHE) can perform computations directly on encrypted data, but currently only supports specific operations. In some simple mathematical operation scenarios, FHE can give full play to its advantages to implement encrypted data computing. “Blind computing” is a more general computing framework, which has the potential to aggregate and apply encryption technologies such as ZKP, TEE, FHE, etc., to explore an integrated engineering practice scheme for privacy protection. Not only can it support multiple types of computing, but it can also integrate the advantages of different encryption technologies to adapt to a wider range of application scenarios.

3.3 Technological Innovation Points and Advantages

The innovation of ‘blind computation’ is first and foremost reflected in its ability to aggregate a variety of cryptographic technologies. It breaks the situation where traditional cryptographic technologies operate independently, integrating multiple advanced cryptographic technologies such as multi-party computation, homomorphic encryption, zero-knowledge proof, etc., forming a synergistic whole. This integrated innovation provides a more powerful solution for privacy protection, meeting strict requirements for data privacy and security in different scenarios.

The enhancement of distributed nodes is also a major innovation of ‘blind computing.’ It enables a single node to have the capabilities of segmented storage + computation simultaneously, combined with a verifiable open governance network, allowing nodes to function effectively without knowing the ‘complete’ data. This design effectively solves the problems of high data transmission costs and privacy leakage in traditional data processing models. In traditional models, protecting data privacy requires multiple encryption, transmission, and decryption of data between different nodes, which is not only costly but also poses a risk of data exposure. ‘Blind computing,’ through the design of distributed nodes, disperses data processing tasks to various nodes, reducing the number of data transmissions and risks, thereby improving the efficiency and security of data processing.

In terms of privacy protection, ‘blind computation’ has significant advantages. It can ensure that data remains encrypted throughout the entire processing process, and even the nodes involved in the computation cannot see the data itself, truly achieving end-to-end privacy protection. This is of great significance for processing sensitive data such as financial data, medical data, etc.

In terms of data processing costs, “blind calculation” optimizes the data processing process, reduces data transmission and multiple encryption and decryption operations, and reduces the consumption of computing resources and time costs. In the cloud computing scenario, users can upload encrypted data to the Nillion network for processing without worrying about the security of the data in the calculation process, while also reducing the costs of data transmission and processing.

In terms of application areas, the versatility of “blind computing” enables its wide application across various fields. In addition to the cryptocurrency sector, it holds significant potential in areas such as artificial intelligence, healthcare, and finance. In the field of artificial intelligence, “blind computing” can protect user privacy data while providing secure data support for model training. In healthcare, it ensures the privacy and security of patient medical records during sharing and analysis processes. In finance, it guarantees the security and privacy of financial transaction data, preventing data leaks and fraud.

4. Nillion Application Scenarios and Cooperative Ecosystem

4.1 Main Application Areas

4.1.1 Web3 Privacy Protection

In the world of Web3, the public transparency of data enhances the trust of the blockchain, but also sacrifices user privacy. Transaction information and data on the blockchain are publicly available in real time, and anyone with internet access and relevant tools can access sensitive information stored on the public blockchain. This is a huge obstacle for users who are sensitive to privacy. For example, in decentralized finance (DeFi) applications, user transaction records and asset information are publicly visible, which may lead to privacy breaches and even security risks.

Nillion’s ‘Blind Computation’ technology introduces private computing capabilities to Web3, effectively solving this problem. It allows users to process sensitive data on-chain without revealing it, ensuring that the data remains encrypted throughout the processing, and even the nodes involved in the computation cannot see the data itself. During the execution of smart contracts, ‘Blind Computation’ can encrypt the data in the contract, making the contract execution more secure and privacy-preserving. In this way, ‘Blind Computation’ expands the design space of blockchain applications, making privacy protection and decentralization possible simultaneously, no longer mutually exclusive. This provides a new solution for users with higher privacy requirements to enter the Web3 world and opens up a broader space for the development of Web3 applications.

4.1.2 AI Field

With the rapid development of artificial intelligence technology, while it provides convenience for people’s work and life, it also brings the risk of privacy leakage. Artificial intelligence requires a large amount of data in the training and reasoning process, which often contains users’ sensitive information, such as transaction information, passwords, identities, and trade secrets. Once these data are exposed in a centralized big company, it will bring huge social hazards. In the application of facial recognition technology, if the data is leaked, it may lead to the theft of user identities; in intelligent medical diagnosis, if a patient’s medical records are leaked, it may pose a serious threat to the patient’s privacy and security.

Nillion’s ‘blind computation’ technology provides an effective privacy protection solution for the AI field. Through ‘blind computation’, AI models can be securely trained and reasoned without exposing the original data. During the data preprocessing stage, data is encrypted using technologies such as homomorphic encryption, and then the encrypted data is input into the AI model for training. During the training process, nodes perform calculations on the encrypted data without the need to decrypt the data, thereby protecting the privacy of the data. In the reasoning stage, ‘blind computation’ technology can also be used to ensure the privacy of input data and output results. This makes AI technology more secure and reliable when handling sensitive data, providing a more solid foundation for privacy protection in AI development.

4.1.3 Financial and Medical Industry

The financial and healthcare industries are two industries that have extremely high requirements for data privacy and security. In the financial industry, customer transaction records, account information, credit data, etc., are all sensitive information. Once leaked, it may lead to customer financial losses and credit risks. In the healthcare industry, patient medical records, diagnostic results, genetic data, etc., involve personal privacy. Leaking this information may have serious impacts on patients’ lives and health.

Secure multiparty computation technology provides a new way to securely process sensitive data for these two industries. In the financial sector, banks and other financial institutions can use secure multiparty computation technology to perform risk assessments, credit approvals, and other business operations without exposing customer privacy. During the credit approval process, banks can send encrypted customer data to multiple nodes for computation. The nodes analyze the encrypted data based on predefined algorithms to assess the customer’s credit risk without the need to know specific customer information. In the medical field, medical institutions can share and analyze medical data using secure multiparty computation technology while protecting patient privacy. Different medical institutions can share encrypted medical data for collaborative research and diagnosis without worrying about the risk of data leakage. This not only helps improve the quality and efficiency of medical services but also promotes the development of medical research, providing better medical services for patients.

4.2 Cooperative Projects and Ecological Construction

Nillion actively lays out in the ecological construction, cooperates with many well-known projects to promote the application and development of ‘blind computing’ technology. In the field of blockchain, Nillion has collaborated with well-known encrypted projects such as NEAR, Aptos, and Arbitrum. On September 13, 2024, Nillion’s privacy feature was integrated with NEAR, enabling over 750 projects in the NEAR ecosystem to access ‘blind computing.’ This collaboration allows Nillion’s privacy protection technology to be integrated into more blockchain applications, providing stronger privacy protection capabilities for these applications, while also expanding Nillion’s user base and market influence.

In the field of AI, Nillion is associated with Ritual, Rainfall, Skillful AI, Nuklai, and Virtuals,io.netProjects such as Capx, Dwinity, Brainstems, etc. have established partnerships. These collaborations aim to explore the application of ‘blind computing’ technology in AI model training, inference, and data privacy protection. Through cooperation with these AI projects, Nillion can integrate its technological advantages with the development needs of AI, providing a more secure, privacy-protective solution for the AI industry, and promoting the healthy development of AI technology.

In the medical field, Nillion has also accumulated multiple partners such as Agerate, Naitur, and MonadicDNA. Through cooperation with these medical projects, Nillion is committed to solving the privacy protection and secure sharing issues of medical data, providing more reliable technical support for data processing and analysis in the medical industry, and promoting the digital transformation and innovative development of the medical industry.

These collaborative projects are of great significance to the ecological construction and business expansion of Nillion. By cooperating with projects in different fields, Nillion can apply its ‘blind computation’ technology to a wider range of scenarios, validating the feasibility and effectiveness of the technology, continuously optimizing and improving it. Collaboration also helps Nillion attract more developers and users, forming a virtuous cycle ecosystem, jointly promoting the development and application of privacy protection technology, and achieving a win-win situation.

5. Nillion Token Economics

5.1 Token Allocation

$NIL is the utility token of Nillion Network, with a total supply of 1 billion, allocated as follows:

5.2 Token Usage

• Ensure coordination layer: Staking NIL tokens can obtain voting rights, used to protect the network and determine the effective validator set through the Delegated Proof of Stake mechanism.

• Manage network resources: Users pay NIL tokens to use the coordination layer or make blind computation requests, thereby promoting efficient resource management.

• Petnet cluster economy: Infrastructure providers join the cluster to facilitate blind computation. They earn NIL token rewards by providing secure storage and resources to the network.

• Network Governance: NIL holders can stake their tokens to vote on on-chain proposals within the coordination layer, or delegate their voting power to others.

6. Nillion Market Performance and Development Trends

6.1 Market Data and Index Analysis

As of now, although Nillion has not yet conducted a TGE (Token Generation Event), it has achieved a series of significant results in technical verification and ecological construction, which can be quantitatively analyzed through some key data and indicators to evaluate its development trend.

In terms of node participation, Nillion’s number of validators is showing a rapid growth trend. As of September 24, the number of Nillion validators reached 75,841, reflecting a high level of market attention and participation in the Nillion project. The participation of numerous validators in the Nillion network not only helps maintain the stability and security of the network but also promotes the decentralized development of the network. A large number of validators means that the network’s computing and storage capacity has been effectively expanded, enabling it to handle more data and tasks, providing a solid foundation for the expansion of Nillion’s application scenarios.

In terms of data processing capability, the total number of challenges to secrets and the amount of protected data are two important indicators. As of a specific time, the total number of challenges to secrets is 37.33 million times, and the total amount of protected data is 513GB. The total number of challenges to secrets reflects the activity and application demand of Nillion Network in privacy protection computing. A large number of challenges indicate that Nillion’s ‘blind computation’ technology has been widely tried and applied in practical applications, with a high market demand for its privacy protection capabilities. The amount of protected data directly reflects the practical application value of the Nillion Network. The 513GB of protected data indicates that Nillion has played an important role in the field of data privacy protection, providing users with secure and reliable data storage and processing services.

There is a close relationship between these data. The increase in the number of validators makes it possible to handle more data, thereby increasing the total number of challenges to secrets and the amount of protected data. The increase in the total number of challenges to secrets and the amount of protected data further proves the practicality and reliability of the Nillion network, attracting more validators to participate and forming a virtuous cycle. From the development trend, with the continuous advancement of the Nillion ecosystem construction, the number of validators is expected to continue to grow, and the data processing capabilities will be further enhanced. The total number of challenges to secrets and the amount of protected data will also increase, laying a more solid foundation for Nillion’s development in the market.

6.2 Future Development Trends Forecast

With the continuous development of technology and the changing market demands, Nillion is expected to achieve breakthroughs and developments in multiple aspects. In terms of technology application expansion, Nillion’s ‘blind computing’ technology has extensive application potential. In addition to the current involvement in Web3 privacy protection, AI field, and the financial and medical industries, it is also expected to be applied in more areas in the future. In the field of Internet of Things, with the widespread popularity of IoT devices, data interaction and privacy protection between devices have become important issues. Nillion’s ‘blind computing’ technology can ensure that IoT devices maintain encrypted data during data transmission and processing, protecting user privacy and security. In the field of supply chain finance, ‘blind computing’ technology can achieve privacy protection of supply chain data, while ensuring that parties can effectively cooperate and transact without revealing sensitive information.

From the perspective of expanding market share, Nillion has established a good ecological foundation in multiple fields through cooperation with many projects. In the future, with the continuous maturity of its technology and the continuous expansion of application scenarios, Nillion is expected to attract more users and partners, further expanding its market share. In the blockchain field, cooperation with projects such as NEAR, Aptos, and Arbitrum enables Nillion’s privacy protection technology to be integrated into more blockchain applications, providing these applications with stronger privacy protection capabilities, thereby attracting more users to use these applications and indirectly expanding Nillion’s user base. In the field of AI, cooperation with projects like Ritual and Rainfall helps Nillion apply its technology to AI model training and inference, meeting the AI industry’s demand for data privacy protection, thereby gaining a foothold in the AI market.

In terms of industry standard setting, as an innovator in the field of privacy protection, Nillion may participate in or even lead the formulation of industry standards in the future. With the growing demand for privacy protection, the industry’s need for unified privacy protection standards is becoming increasingly urgent. With its advanced technology and rich practical experience, Nillion is expected to play an important role in the formulation of industry standards, promoting the standardization and healthy development of the entire privacy protection industry. By setting industry standards, Nillion can not only enhance its position and influence in the industry, but also provide strong support for the promotion of its technology and products, further consolidating its market competitive advantage.

Conclusion

Nillion demonstrates significant advantages in the field of privacy protection and data security with its innovative ‘blind calculation’ technology. This technology achieves efficient processing of data in encrypted state by aggregating multi-party computation, homomorphic encryption, and other advanced encryption technologies, providing users with unprecedented privacy protection solutions.