Will AI Privacy Blind Computing Trigger a Web3 User Surge?
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Will AI Privacy Blind Computing Trigger a Web3 User Surge?
This article introduces the new Web3 concept of blind computing and how it protects our data privacy.
Navigating Web3 tides with focused insightsAuthor: Viee, Core Contributor of Biteye
Editor: Crush, Core Contributor of Biteye
Community: @BiteyeCN
*Approximately 3000 words, estimated reading time 6 minutes
Would you be willing to hand over ten years of your personal conversation data to OpenAI, Google, or Facebook?
Imagine a future where AI assistants perfectly replicate your way of thinking and act just like you when handling daily tasks. While exciting, this also implies that AI systems would need access to vast amounts of data—every message you’ve ever sent, and all the information that makes up your unique personality. This brings us back to the question posed at the beginning. According to surveys, 59% of consumers feel uneasy about using personalized AI, primarily due to concerns over data privacy.
Nillion, an innovative decentralized network, offers a practical solution to this challenge by leveraging multi-party computation (MPC) and other privacy-enhancing technologies (PETs). In this article, Biteye will introduce Web3’s emerging concept of blind computing and explain how it safeguards our data privacy.
01 Current State of Data Privacy and Security
Data is often referred to as the new "oil" in the digital age, making privacy and security increasingly critical. Traditional data processing methods typically require decrypting data before computation, exposing sensitive information to potential threats during processing. For instance, in healthcare, patient data must be strictly protected, yet risks of exposure remain during analysis. This not only undermines user trust in services but also limits opportunities for data sharing and collaborative research.
Despite the immense potential of personalized AI, data privacy must be seriously addressed before this vision can be realized—only then can personalized AI truly usher in the era of the “next internet.”
02 What is Blind Computing?
Nillion proposes a novel approach to solving these challenges: “blind computing.” Through a decentralized network architecture and advanced privacy-enhancing technologies, blind computing enables high-value data to be securely stored and processed without decryption.
Blind Computing allows users to perform computations without directly accessing the original data. This means users can safely operate even when data is stored in untrusted environments.
The process mainly includes:
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Data is masked and split into parts
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These fragments are sent to different nodes
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Nodes process the data without seeing its contents
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Results are collected and combined
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Only authorized parties can view the final output
In essence, blind computing processes encrypted data. More specifically, users encrypt their data and send it to cloud servers or other computing platforms. All computations on these platforms are performed directly on the encrypted data, producing an encrypted result. After receiving the result, users decrypt it to obtain the final answer—without ever knowing any intermediate details. Like an “invisible computing assistant,” hence the term “blind computing.”
Blind computing integrates multiple advanced technologies to ensure sensitive information remains secure throughout processing:
1. Multi-Party Computation (MPC)
MPC is a technique that enables multiple parties to jointly compute a function without revealing their individual inputs. Each participant only knows their own input and the final result, with no access to others’ data.
The principle of MPC can be illustrated by the classic Millionaire Problem, first proposed by Andrew Yao in 1982. Suppose two millionaires want to know who is richer without disclosing their actual wealth. Using MPC, they can jointly compute the comparison through a series of cryptographic operations, revealing only which one is wealthier while keeping exact net worth private. This ensures information security while enabling collaboration.
This is achieved through cryptographic protocols allowing each party to input their net worth into a shared computation. The system is designed so that only the comparison outcome (i.e., who is richer) is revealed, with no details about their actual assets disclosed. This demonstrates MPC’s power: enabling collaborative computation while preserving privacy.
Applications: In blind computing, MPC ensures that even when computations occur on cloud servers or untrusted environments, participating nodes cannot see the raw data. This makes it ideal for handling sensitive data such as financial transactions or medical records.
2. Homomorphic Encryption
Homomorphic encryption is a special form of encryption that allows direct computation on encrypted data without decryption. Users can perform operations (e.g., addition and multiplication) on ciphertext, and the result remains encrypted. Only the user with the correct key can decrypt it to obtain the correct answer.
Applications: Homomorphic encryption plays a crucial role in blind computing, enabling servers to compute on encrypted data without accessing the underlying information. This significantly enhances data security in cloud environments.
3. Privacy-Enhancing Technologies (PETs)
PETs refer to a suite of methods designed to strengthen personal privacy protection, including anonymization, pseudonymization, and data masking.
Applications: In blind computing, PETs work alongside MPC and homomorphic encryption to further enhance data security and privacy during processing. For example, anonymizing input data prevents participants from identifying data sources.
4. Quantum Blind Computation
Quantum blind computation leverages principles of quantum computing to achieve blind computing. It enables users to perform encrypted computations on quantum computers, protecting both input and output data privacy.
Applications: Still in the research phase, quantum blind computation holds promise for solving more complex problems and potentially expanding users' computational capabilities in cloud environments once realized.
03 Nillion's Dual-Network Architecture
To integrate these technologies and enable blind computing, Nillion employs a dual-network architecture consisting of a coordination layer (NilChain) and an orchestration layer (Petnet). This design ensures efficient data storage and processing while maintaining system security and privacy.
1. Coordination Layer (NilChain)
The coordination layer manages payment operations within the network, including storage and blind computing. It ensures smooth transaction processing and effective resource allocation.
2. Orchestration Layer (Petnet)
The orchestration layer uses MPC and other PETs to protect data at rest and enable blind computation on that data. Petnet maintains high levels of security and privacy even when data is shared across multiple nodes. This layer provides developers with a flexible platform to build diverse applications tailored to various needs.
04 Nillion’s Current Progress
On October 30, Nillion announced a $25 million funding round led by Hack VC, with support from Arbitrum, Worldcoin, and Sei. To date, Nillion has raised a total of $50 million.
Since launch, Nillion has achieved impressive metrics:
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Number of validators: 247,660
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Total protected data: 711 GB
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Total secrets challenged: 120,254,931
Validators help maintain data security and integrity. Growth in their number indicates that the Nillion Network is becoming stronger and more secure.
Nillion’s current partners include blockchain networks such as NEAR, Aptos, Arbitrum, Mantle, IO.net, and Ritual. These collaborations span multiple domains: Ritual and Nesa use Nillion for private AI model training and inference; Rainfall, Dwinity, and Nuklai leverage it to store, share, and monetize AI training data; MIZU uses it to generate synthetic data while protecting personal data. Virtuals Protocol, Capx AI, and Crush AI are building personalized private agents powered by Nillion. PINDORA utilizes Nillion for confidential and secure support in DePIN networks. Nillion aims to attract projects at the intersection of blockchain and AI that require secure data sharing and storage.
In the future, we can expect Nillion to find broad applications in healthcare, finance, education, and beyond, contributing to a safer and more transparent data ecosystem.
05 Conclusion
Through its innovative technical architecture and robust privacy protection capabilities, Nillion offers a viable path toward addressing data privacy challenges in today’s digital world—enabling users to enjoy the convenience of digital services without fear of personal information being leaked or misused.
Today, we can hardly imagine the full scope of AI’s future. The rise of personalized digital avatars and growing concerns over data privacy resemble two ends of a seesaw. Without effective data privacy protections, personalized AI will struggle to gain widespread market acceptance. Therefore, finding the right balance between technological advancement and user privacy protection remains a critical challenge for the industry. As the Nillion network evolves, we look forward to seeing more innovative applications emerge, bringing positive impacts to human society in the AI era.
💡 Risk Disclaimer: This article is for informational purposes only and does not constitute investment advice. Please comply with local laws and regulations.
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