Nillion, as an important driver in the field of secure computing, is unlocking the potential of AI across various industries.
Author: 3rd Street Capital
Translated by: Deep Tide TechFlow
The Growing Importance of Secure Computing
In today's increasingly digital world, the risks of data breaches and misuse of personal information have significantly increased. These threats not only harm businesses but also pose substantial risks to individuals. For example, in the Cambridge Analytica incident, Facebook allowed a political consulting firm to access millions of users' personal data without their consent, influencing major political events. This incident revealed the dangers of centralized data control and highlighted the vulnerability of personal information in the current digital environment.
In addition to data misuse issues, large-scale data breaches have further exposed the inadequacies of current data protection methods. Here are some notable breaches in recent years:
Ledger (2020): Personal information of 270,000 customers was exposed
LinkedIn (2021): Data of 700 million users was leaked
LastPass (2022): Hackers breached encrypted password vault
Microsoft (2023): Data of 65,000 companies was exposed
The increasing number of data breaches and misuse cases emphasizes the urgent need for enhanced security measures. As artificial intelligence (AI) becomes more integrated into our daily lives, a key question arises: how can we trust AI to protect our most sensitive information?
Secure computing technology has the potential to revolutionize AI, enabling it to provide highly personalized and secure services while protecting privacy.
Unlocking AI's Potential Through Secure Computing
Even advanced AI tools like ChatGPT remind users not to share sensitive or personal information, highlighting the risks involved in data processing. Recent lawsuits against ChatGPT and Microsoft's Copilot have intensified this warning, accusing them of using customer data to train AI models without consent. These cases have raised serious concerns about data privacy and trust:
OpenAI and Microsoft sued for $3 billion over ChatGPT's "privacy violations"
OpenAI faces lawsuit for allegedly using data to train ChatGPT without authorization
Unfortunately, data privacy and security issues hinder the immense potential of AI. However, imagine a future where machine learning AI can securely process sensitive data through advanced encryption techniques, ensuring that information is not leaked or exposed. This would bring revolutionary changes across various industries and improve people's lives.
This is where secure computing can play a role. It is the key to unlocking the full potential of AI, allowing it to provide personalized and customized services while ensuring data privacy and security are not compromised.
What is Secure Computing?
To understand how Nillion is driving this innovation, it is essential to grasp the concept of secure computing and how it works. Secure computing enables AI to perform machine learning operations on encrypted data, meaning that even sensitive information can be processed without being exposed. For instance, tools like ChatGPT can handle encrypted data just as they would regular text. The entire computation process occurs in a secure environment, with output results remaining encrypted until decrypted by the key holder—ensuring that your data remains private throughout the process.
Through secure computing, AI can provide powerful and personalized services without sacrificing data privacy and security, making it a key technology for the future of machine learning.
How Secure Computing Works
Imagine a group of friends, each with a secret ingredient for a special recipe, but they are unwilling to share their ingredients with one another. They all want to know what the final dish would taste like if all the ingredients were combined.
Here’s how they do it:
Secret ingredients placed in locked boxes: Each friend puts their secret ingredient into a locked box, with only they having the key, so others cannot see the contents.
Magic chef (secure computing): They hand all the locked boxes to a magic chef, who can cook with these ingredients without opening the boxes. The chef has special tools to mix, bake, or sauté the ingredients inside the boxes without needing to unlock or view the contents.
Final dish in a sealed container: Once cooking is complete, the magic chef places the final dish in a sealed container that only the friends can open together.
Sharing the deliciousness: When they open the container, they can taste the deliciousness of all the secret ingredients combined. They enjoy the dish and understand how each of their contributions worked together, but no one knows what the individual secret ingredients are.
This story vividly illustrates secure computing:
Locked boxes (data encryption): The secret ingredients are like encrypted data—protected and inaccessible to others.
Magic chef (secure computing algorithms): The magic chef represents algorithms that can compute on encrypted data without needing to decrypt it.
Final dish (computation result): The final dish in the sealed container is like the encrypted result of the computation, accessible only to authorized individuals who can decrypt or access it.
Privacy always protected: Throughout the process, each friend's secret ingredient remains confidential, but they all benefit from the combined result.
In simple terms:
Secure computing allows computers to perform operations on encrypted data, meaning that this data appears as meaningless characters to them. Computers can process and combine this encrypted data to generate useful results without knowing the actual content of the data. Only those with the correct "key" can unlock and understand the final result, ensuring that everyone's privacy is protected.
Is Secure Computing the Same as Zero-Knowledge Proof (ZK)?
When discussing secure computing, people often ask if it is similar to zero-knowledge proof (ZK). While ZK and secure multi-party computation (sMPC) are both privacy-enhancing technologies, they serve different purposes.
Zero-knowledge proof allows one party to prove to another that a statement is true without revealing any additional information. For example, you can prove you have enough account balance without disclosing the exact amount. Secure computing, on the other hand, allows multiple participants to collaboratively compute a function on their combined data without revealing their individual inputs to each other.
In short, ZK proofs focus on securely proving facts, while MPC, as illustrated by the magic chef example, focuses on secure collaborative computation without exposing personal inputs. While both ZK and MPC are powerful privacy protection tools, their applications differ: ZK is more about securely proving facts, while MPC enables secure collaborative data processing.
The Mathematical Principles of MPC
In the magic chef example, we see how multiple participants can contribute data without revealing it while still creating meaningful results. This is the working principle of multi-party computation (MPC): it securely processes hidden data through clever mathematical methods.
If you are interested in the mathematical principles of MPC, you can watch the video series “MPC Explained”. It explains these concepts in an intuitive way that even those without a background in cryptography can easily understand.
Driving Industry Transformation Through Secure Computing
The principles of secure computing are not just theoretical; they have transformative potential across various industries. Here are some practical examples of how secure computing is bringing revolutionary changes in key areas such as healthcare, finance, and personal data management:
Applications in Healthcare:
Hospitals can use secure computing to safely share encrypted genetic data from millions of patients worldwide. AI can then analyze this data to identify new genetic markers for diseases like Alzheimer's and develop predictive models. This will significantly enhance early detection and prevention while ensuring complete confidentiality of patient data.
Applications in Financial Services:
Financial institutions can collaborate with regulators through secure computing to analyze encrypted transaction data, enhancing fraud detection and compliance monitoring. For example, banks can securely share encrypted data of large-scale transaction patterns with an AI-driven central regulatory platform. This platform can analyze aggregated encrypted data from multiple banks to identify systemic risks, detect fraudulent activities like money laundering, and monitor compliance with financial regulations.
By using secure computing, banks can collectively enhance the financial system's resilience against fraud and systemic risks while protecting customer privacy and business secrets.
Personal Application Scenario 1 (Healthcare):
Individuals can securely share their encrypted genetic data with AI-driven platforms to receive highly personalized health plans. These plans may include tailored nutrition and fitness programs designed to predict and prevent chronic diseases, even before symptoms appear, while ensuring complete confidentiality of sensitive health data.
Personal Application Scenario 2 (Tax Audits):
Individuals and businesses can securely share their encrypted financial data with AI-driven auditing platforms. These platforms utilize secure computing to conduct comprehensive tax audits without disclosing sensitive financial information.
For example, taxpayers can upload encrypted financial records to the platform, which analyzes the data to check for tax law compliance, identify discrepancies, and provide optimization suggestions for tax filings. The platform generates detailed reports and recommendations while ensuring the confidentiality of the actual financial data, preventing auditors or third parties from seeing it. This process reduces the need for manual audits, saves time and costs, improves accuracy, and ensures privacy.
These examples demonstrate how secure computing can unlock the potential of AI, providing powerful solutions across various industries while maintaining the highest standards of privacy and security.
Practical Applications of Privacy-Enhancing Technologies
Secure computing may sound like a concept of the future, but some of the world's largest organizations are already using privacy-enhancing technologies (PETs) in practice. For instance, JP Morgan employs multi-party computation (MPC) in its dark pool trading system, allowing parties to conduct private trades without exposing sensitive information such as inventory positions. Similarly, Meta uses MPC in its advertising technology for private effect measurement, enabling advertisers to assess the effectiveness of campaigns without accessing users' private data.
These examples highlight how the principles behind secure computing have been successfully applied to address real-world privacy challenges, showcasing the feasibility and impact of privacy-enhancing technologies in modern industries.
Nillion: A New Era of Secure Computing
Introducing Nillion: A New Era of Secure Computing
Nillion, as an important driver in the field of secure computing, is unlocking the potential of AI across various industries. While Nillion is not the only secure computing solution, it stands out with its decentralized network powered by Petnet. This network processes private data through secure multi-party computation (sMPC) and combines it with Nil Message Compute (nMC) to provide efficient scalability.
Core Elements of the Nillion Architecture
Overview of the Nillion Architecture
Petnet — A Network of Enhanced Privacy Technologies
At the heart of Nillion's infrastructure is Petnet, a decentralized network of securely connected nodes. Petnet allows data to be processed in a distributed manner, utilizing sMPC to enable multiple participants to perform joint computations without disclosing private data. nMC further optimizes this process by reducing communication between nodes, enhancing efficiency and scalability.
Petnet also employs data sharding technology, breaking down high-value data (HVD) such as personal identities, medical records, proprietary algorithms, and financial transactions into smaller encrypted fragments distributed across different nodes. This ensures that no single node can access the complete dataset, significantly enhancing security and performance.
Dual Network System — Coordination Layer and Orchestration Layer
Built on Petnet are the coordination layer and orchestration layer:
Coordination Layer (NilChain): Responsible for task allocation within the network, ensuring each node understands its role in the computation and coordinating storage operations and payments for blind computations. This layer ensures that economic incentives align with efficient management of network resources.
Orchestration Layer: Responsible for coordinating and integrating various privacy-enhancing technologies (PETs) to achieve complex and secure computations. It acts like a "conductor," managing and merging different cryptographic techniques.
NilVM — Nillion Virtual Machine
NilVM is the execution environment for blind computation programs written in Nillion's programming language, Nada. It ensures that these programs can run efficiently and securely within the Nillion network, optimizing the application of various privacy-enhancing technologies (PETs) through the orchestration layer.
NIL Token
NIL Token is the native utility token of the Nillion network. Its uses include:
Security and Coordination: By staking NIL tokens, users can gain voting rights to delegate proof-of-stake mechanisms that secure the network and decide on the selection of active validators.
Managing Network Resources: Users pay with NIL tokens to utilize the coordination layer or initiate blind computation requests, effectively managing network resources.
Petnet Cluster Economy: Infrastructure providers join clusters to support blind computations and earn NIL token rewards by providing network resources.
Network Governance: NIL holders can stake tokens to vote on on-chain proposals or delegate their voting rights to others.
A New Era of AI: Nillion's Transformation in Secure Data Processing
Nillion is leading a transformation in data security by integrating advanced privacy-enhancing technologies (PETs) such as secure multi-party computation (sMPC) and fully homomorphic encryption (FHE) through its innovative orchestration layer. Here are two examples of how Nillion's technology is making a profound impact in different fields.
- Healthcare Use Case: Accelerating the Development of a Common Cold Vaccine
Challenges Faced
New strains of the common cold virus emerge every year, complicating vaccine development. Researchers need access to a vast array of diverse datasets to analyze these constantly changing viruses. However, privacy laws only permit access to patient data that has been explicitly consented to, limiting sample sizes and slowing down vaccine development.
Nillion's Solution
Nillion's secure computing technology eliminates trust issues in data sharing through the coordination of its orchestration layer. By integrating secure multi-party computation (sMPC) and fully homomorphic encryption (FHE), Nillion enables hospitals to securely share encrypted patient data. Researchers can analyze this data without disclosing personal information, facilitating secure and private collaboration on a larger scale.
Impact Achieved
With Nillion, researchers can access a large volume of encrypted patient data, significantly accelerating vaccine development. The secure utilization of nearly limitless datasets makes it possible to quickly identify vaccine targets and better respond to new strains while ensuring patient privacy is protected.
Why Existing AI Models Cannot Achieve This
Current AI models rely on trust and require patient consent to share raw data, limiting participation and sample sizes. Data breaches and privacy issues remain significant challenges for platforms like ChatGPT and other large language models (LLMs). Without secure computing, these models cannot ensure that sensitive information is not leaked or misused, hindering the potential for collaborative research while protecting patient privacy.
- Personal Use Case: Secure Personal AI Writing Assistant
Challenges Faced
Many people often need AI assistance when writing emails, editing sensitive reports, or summarizing personal documents. However, privacy concerns and the risk of data breaches make users hesitant to entrust confidential information to AI platforms. Current AI systems require access to raw data, raising concerns that personal documents may be stored, leaked, or misused.
Nillion's Solution
Nillion provides a secure AI writing assistant that ensures user privacy at every stage through its orchestration layer:
Orchestration of Document Submission: Users can securely submit documents through Nillion's orchestration layer, which manages the encryption process to keep the content confidential, even from the AI service itself.
Secure Computing: Supported by the Nillion network, AI uses secure multi-party computation (sMPC) and fully homomorphic encryption (FHE) to process documents. The orchestration layer coordinates these technologies, allowing AI to edit, summarize, and improve content without decrypting the original document.
Confidential Result Delivery: The results processed by AI are returned to the user in encrypted form. Only the user can unlock and view the final results using their private decryption key.
Impact Achieved
This approach allows users to confidently use AI for writing tasks while protecting their sensitive information. Nillion enables AI to handle tasks that were previously off-limits due to privacy concerns, such as processing legal documents or confidential business reports, without compromising user trust or data security.
Why Existing AI Models Cannot Achieve This
Existing AI models require access to raw text data, which poses significant privacy risks. Users must trust that the platform will not misuse or leak their sensitive documents. Nillion eliminates this need for trust by allowing AI to process encrypted data directly, ensuring privacy and confidentiality throughout the entire process.
Conclusion: Nillion Leading the Future of Secure Computing
As the digital world continues to evolve, the risks of data breaches and misuse of sensitive information are also increasing. The growing data privacy issues indicate an urgent need for stronger protective measures. At the same time, artificial intelligence has immense potential to transform industries, but its potential has not been fully realized due to privacy concerns and vulnerabilities in current data processing methods.
Currently, AI is limited by concerns over privacy and sensitive information handling, with its applications often restricted to non-critical areas. However, breakthroughs in secure computing will enable AI to safely process sensitive data, unlocking its true potential. This will be a turning point, allowing AI to integrate into all aspects of our daily lives, fundamentally changing industries and our interactions with technology.
Nillion is leading this transformation. With its ability to perform secure computations on encrypted data, Nillion addresses the challenges that hinder the widespread application of AI. By adopting advanced privacy-enhancing technologies such as secure multi-party computation (sMPC) and fully homomorphic encryption (FHE), Nillion ensures that sensitive information is protected throughout the process. From facilitating secure global collaboration in healthcare to providing more personalized and secure financial services, Nillion unlocks the full potential of AI while safeguarding privacy and security.
Join Nillion, explore their website, read detailed information about their groundbreaking technology in the documentation, and follow them on X for the latest updates to be part of shaping the future of secure computing:
Together, we can create a digital world where privacy and innovation coexist.
Acknowledgments
I would like to express my sincere gratitude to the Nillion team for the graphics and valuable feedback they provided. I also thank the friends from 3rd St Capital and uDAO for their valuable insights and support in the conception of this article. For more information about 3rd St Capital, please visit our website:
For more information about uDAO, please visit:
Important Disclaimer
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