As various AI technology models mature, AI is being widely applied in many fields including AIGC, autonomous driving, healthcare, big data analysis, and automobile manufacturing. It intelligently executes operations in certain scenarios through computation and analysis, leading to a qualitative leap in production and work efficiency.

However, while AI computation brings efficiency improvements, it also poses the risk of data leakage. AI computation relies on data from different fields as fuel for analysis and execution, but this data may involve personal and commercial secrets, such as medical records, financial information, personal identification information, and automobile manufacturing parameter data, triggering concerns about data security in the AI era.

For example, last year, Microsoft's AI research team accidentally leaked a large amount of data, including user information, chat records, and sensitive emails. Although the incident did not escalate further, many tech companies began to re-examine AI data security issues. Another example is the ChatGPT model, which has outstanding AIGC capabilities and was accused by the Italian data protection authority of illegal collection of user data, violating GDPR. In a report on the investigation into a temporary interruption of ChatGPT Plus services on March 25 this year, OpenAI officially admitted that about 1.2% of ChatGPT Plus user data may have been leaked.

Privasea has proposed a machine learning solution based on Fully Homomorphic Encryption (FHEML) technology, which allows AI to process and analyze encrypted data without decryption, eliminating the risk of data leakage in AI computation and machine learning processes. This solution meets various compliance requirements, including the General Data Protection Regulation (GDPR) of the European Union. Meanwhile, Privasea provides strong support for the massive computing resources needed in the network by building a DePIN crowdfunding computing power network. Privasea has found a new balance in data protection, AI computation, machine learning, computing power supply, and compliance.

Recently, Privasea, an early investment project of Binance Labs, has attracted attention in the AI and Depin fields as well as the encryption market due to its innovative FHEML solution. The project has received two rounds of strategic financing, including a total of $5 million in pre-seed/seed round financing, and a new round of strategic private placement financing with participation from OKX Ventures, Laser Digital controlled by Nomura Group, and Tanelabs, a subsidiary of SoftBank.

This article will further introduce the Privasea project to enhance readers' understanding of Privasea.

1. Why can FHE technology become an important solution to eliminate the risk of data leakage in the AI field?

FHE (Fully Homomorphic Encryption) allows calculations on encrypted data, transforming data into a mathematical structure that enables computation while maintaining encryption. This means that processing and analysis can be performed on encrypted data, and the results can be safely returned to the data owner, who is the only one able to decrypt and view the final results.

The core advantage of this solution is that it provides unprecedented protection for data security and is particularly suitable for AI computation and machine learning. For example, in an AI environment, users can upload encrypted data to AI (or cloud) for storage and computation without worrying about unauthorized access to their sensitive information by cloud service providers or other third parties. Additionally, even if data is intercepted during transmission, attackers cannot understand the content of the data without the corresponding decryption key.

Compared to other solutions such as ZKP (Zero-Knowledge Proof), MPC (Multi-Party Computation), and TEE (Trusted Execution Environment), FHE is more suitable for constructing self-custodial data solutions in the AI field and is considered by many to be the end game of cryptography.

Of course, the FHE solution itself also presents certain challenges. In this highly complex mathematical structure, simple arithmetic operations on encrypted data become very complex, and maintaining this structure requires a large amount of computing resources. Therefore, the operational efficiency of the FHE solution requires substantial computing power as support, and the huge consumption of computing resources will also bring significant computing resource costs. The good news is that Privasea is addressing the computing resource challenges faced by the FHE solution through the construction of a DePIN system and is promoting the large-scale adoption of FHE in the AI field.

2. Privasea: DePIN AI Computing Network based on FHEML Solution

As mentioned earlier, the Privasea network is based on the FHEML technology solution and introduces a blockchain incentive layer to continuously obtain computing resources from decentralized resource points, aiming to solve the potential data leakage problems in the AI field and become the most secure AI ML solution. At the same time, Privasea's off-chain data security solution is currently able to comply with regulations, such as the General Data Protection Regulation (GDPR) of the European Union.

In the Privasea network system, there are four important components that work together to provide secure and private AI capabilities:

• Privasea FHE Pipeline

The Privasea FHE pipeline is the core component of the Privasea network. This library is built on top of Zama's THFE-RS library and has been specially customized to better meet the needs of the Privasea project. By leveraging the powerful features of Zama's THFE-RS library, the Privasea FHE library can provide a secure and efficient Fully Homomorphic Encryption (FHE) solution to protect user data.

• Privasea API

The gateway to the Privasea AI network, the Privasea API provides developers with an application programming interface to integrate AI capabilities that protect data security into their applications. This component provides a range of tools and functions to seamlessly interact with the network.

• Privanetix

Privanetix is a decentralized computing node network that utilizes the power of numerous computing nodes to facilitate the secure and efficient processing of encrypted data. These high-performance computing nodes work together to securely execute critical machine learning algorithms. Each node in Privanetix is equipped with an FHEML pipeline suitable for various task models and can efficiently perform inference operations on encrypted data to support efficient collaborative AI applications while maintaining data confidentiality.

• Privasea Smart Contract Suite

The Privasea smart contract system is the incentive driver for Privanetix nodes, effectively tracking the registration and contributions of Privanetix nodes, verifying their computations, and allocating rewards accordingly. By utilizing smart contracts, this mechanism ensures transparency, fairness, and actively incentivizes participation within the network, guaranteeing the computational performance of the Privasea network. Additionally, this component is economically based to prevent malicious behavior by Privanetix nodes.

Based on this system, Privasea has found a new balance point for AI computation, user data security, and distributed computing resources. In addition to its general solution, its customized solutions also possess two important characteristics: efficiency and user-friendliness. Even users without cryptographic or programming skills can easily access the network's capabilities, allowing them to perform FHE AI computations without specialized knowledge.

So based on the Privasea network, users can easily use the FHE solution to encrypt their data or models and upload them to the Privasea AI network. After successful upload, users can access the distributed computing resources in the network to perform machine learning or other computations on their data in an encrypted state. The network supports various computing models, including neural networks, decision trees, clustering analysis, and other models, which can be publicly available on the network or provided by users.

Currently, the Privasea network is also integrating with the distributed storage chain BNB Greenfield, meaning that data in the ecosystem will be stored in a distributed manner through BNB Greenfield. This means that users have absolute control over their data and can flexibly utilize it. They can upload their personal models, whether public or encrypted information, to the network and store them in a distributed manner. The encrypted results can be returned to the user or shared with others using FHE key transformation. This will further provide a secure way to share encrypted data, achieve data value circulation, protect user data, and promote data value sharing.

3. Compliance-oriented off-chain data computing solution

The characteristic of the Privasea network is off-chain data security, completely unrelated to asset transactions, and able to accommodate review features rather than being completely resistant to review on-chain. This approach strictly protects user data security through cryptography and can support compliance audits when needed to meet any country's legal requirements in AML and anti-money laundering laws.

At the same time, the Privasea network can comply with various regulations, including the General Data Protection Regulation (GDPR) of the European Union, which imposes strict requirements on the collection, processing, and storage of personal data. The off-chain nature of Privasea ensures the protection of personal data during model training and inference processes, without collecting human identity information data similar to Worldcoin.

Another key goal of the Privasea network is to protect users' sensitive data from unauthorized access. By using FHE to encrypt sensitive data during AI computation and learning, the network acts as a strong barrier to prevent data leakage and unauthorized intrusion, further enhancing data security through cryptographic techniques.

4. Potential use cases of the Privasea system

The Privasea solution can be highly integrated with many scenarios that require verification and computational analysis, for data protection. Potential scenarios include biometrics, healthcare, finance, secure cloud data computing, and anonymous voting systems.

• Biometrics

Currently, based on the Privasea technology solution, the first biometric (facial recognition) application is about to enter the market. In this facial recognition application, the client is securely nested on the user's device, and based on FHE technology, the client's key is protected through encryption, while the server's data computation process is permanently encrypted, ensuring the protection of personal information during encrypted facial comparison.

In this example, when a user uploads their facial feature photos through the client, the system does not send the original images, but locally converts them into encrypted vectors, preserving unique attributes. These vectors are securely encrypted using the client's key and then transmitted to the backend server of Privasea for strict protection. This means that the original images are shielded, and users can trust the Privasea AI database.

When the user matches the alternative image with their facial features, the client extracts the facial features locally, embeds the vector in an encrypted form, and securely sends it to the server. In the ciphertext domain, the server performs the facial matching algorithm while maintaining data confidentiality. After careful processing, the server provides encrypted results, which can be decrypted using the client's key for confirmation.

Throughout the process, the facial data stored in Privasea (in fact, this encrypted data is stored in a distributed manner) exists in encrypted form and undergoes facial comparison in an encrypted state based on the FHE solution, without leaking the original facial data, ensuring data security. Based on this solution, a series of similar applications, including Proof of Human and Secure KYC, can be further derived.

• Healthcare

Potential applications of the Privasea network can include medical image processing, such as using its high-performance computing resources to analyze medical diagnostic images. With the Privasea network system, healthcare professionals and researchers can use the distributed computing resource network to process medical images while maintaining patient data security.

For example, radiologists can use the Privasea AI network to process a large dataset of medical images for a study. The network can be used to distribute processing workloads across multiple nodes and combine results to improve analysis accuracy. During processing and analysis, patient data will be encrypted and protected.

Scanned medical imaging data, including X-rays or MRIs, can be encrypted using the FHE solution and stored or transmitted in encrypted form. Through Privasea AI, the encrypted medical images can be processed, providing a distributed computing resource network for AI processing. This approach effectively protects patient data and is expected to enable effective training of AI models. After medical processing is completed, the encrypted medical images can be decrypted for use by healthcare professionals.

By combining FHE encryption and the Privasea AI network, medical images can be processed securely and efficiently while protecting patient data. This system not only provides a scalable and cost-effective solution in the medical imaging field but also enhances patient trust. The adoption of cutting-edge technology is expected to further improve medical efficiency and standards.

In the financial sector, the Privasea network, based on the PHE solution and the Privasea AI network, can also establish data protection for various scenarios, including bank transactions and loan reviews, and is expected to further improve the accuracy and efficiency of financial institutions' business operations, significantly reducing costs.

Currently, Privasea has been included in the Google Cloud Web3 Startup Program, which means that the FHE AI network of Privasea is expected to be further integrated with better Google Cloud service to serve more potential use cases.

In addition, as a leader in the FHE technology field, Privasea has been committed to promoting the further adoption of cryptographic technology in the encryption industry. It is reported that at the end of March this year, Privasea, together with leading Web3 FHE track startups such as Zama, conducted a closed-door academic seminar with the theme of "dedicated to the research, development, and application promotion of FHE in web2 and web3". Previously, this series of technical conferences has been held for three consecutive years and has continuously made breakthroughs in innovative FHE technology solutions.

5. Future Outlook

In the development process of AI technology, the potential risk of data leakage is becoming the biggest obstacle. Many people believe that the development of AI is even challenging data laws and regulations, making it difficult for efficient AI technology to be implemented in many potential application scenarios. The lack of computing resources is also a crucial factor that makes it difficult to effectively train AI models and scale AI technology.

The Privasea system, based on the FHEML solution, supports complex calculations directly on encrypted data, providing data protection for various scenarios while also being auditable and compliant with various data regulations. At the same time, Privasea introduces a distributed computing network called Privanetix, which incentivizes different computing nodes to bring dispersed computing resources into the network in a DePIN manner, establishing a Web3-based computing power crowdfunding network. This will provide continuous computing power support for FHE encrypted operations, AI model training, and computation, leading a new revolution in DePIN AI computing power crowdfunding.

Under the promotion of the Privasea network, the FHE solution is expected to be widely adopted and become the mainstream solution in the encryption field. AI technology can also be deeply integrated with various scenarios, being widely adopted while ensuring data security and compliance with data regulations, and being compatible with laws and regulations, to better serve as a tool to enhance human productivity.

At the same time, the Privasea network is also building a compliance-oriented data value circulation system, establishing true user data sovereignty, allowing data owners to control the value of data, and constructing a new paradigm of data value system. Based on this, Privasea is expected to become a new value carrier in the trillion-dollar application market, continuously highlighting its value.

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