An in-depth interpretation of Privasea: Facial data casting NFT, an interesting innovation?

1. Introduction

Recently, a face NFT casting project initiated by Privasea has become extremely popular!

It seems very simple at first glance. In the project, users can enter their faces on the IMHUMAN (I am human) mobile application and cast their face data into an NFT, which is just the face data. The combination of chain + NFT has enabled the project to obtain more than 20W+ NFT casting volume since its launch at the end of April, and the popularity is evident.

I am also very confused, why? Can facial data be uploaded to the blockchain no matter how big it is? Will my facial information be stolen? What does Privasea do?

Wait, let us continue to research the project itself and the project party Privasea to find out.

Keywords: NFT, AI, FHE (Fully Homomorphic Encryption), DePIN

2. From Web2 to Web3-human-computer confrontation never stops

First of all, let’s explain the purpose of the face NFT casting project itself. If you think this project is simply about casting face data into NFT, you are totally wrong.

The App name of the project we mentioned above, IMHUMAN (I am human), already illustrates this problem very well: in fact, this project aims to use face recognition to determine whether you are a real person in front of the screen.

First of all, why do we need human-machine recognition?

According to the 2024Q1 report provided by Akamai (see appendix), Bot (an automated program that can simulate humans sending HTTP requests and other operations) accounts for an astonishing 42.1% of Internet traffic, of which malicious traffic accounts for 27.5% of the entire Internet traffic. %.

Malicious Bots may bring catastrophic consequences such as delayed response or even downtime to centralized service providers, affecting the experience of real users.

Let’s take the ticket grabbing scenario as an example. By creating multiple virtual accounts to grab tickets, cheaters can greatly increase the probability of successful ticket grabbing. Some even directly deploy automated programs in the service provider’s computer room. Next to it, you can purchase tickets with almost zero delay.

Ordinary users have almost no chance of winning against these high-tech users.

Service providers have also made some efforts in this regard. On the client side, in the Web2 scenario, real-name authentication, behavior verification codes and other methods are introduced to distinguish humans and machines. On the server side, feature filtering and interception are carried out through WAF policies and other means. .

Will this problem be solved?

Obviously not, because the benefits from cheating are huge.

At the same time, the confrontation between man and machine is continuous, and both cheaters and testers are constantly upgrading their arsenals.

Take cheaters as an example. Taking advantage of the rapid development of AI in recent years, the client's behavioral verification code has almost been dimensionally reduced by various visual models. AI even has faster and more accurate recognition capabilities than humans. This forces the verifiers to passively upgrade, gradually transitioning from early user behavioral feature detection (image verification code) to biometric feature detection (perceptual verification: such as client environment monitoring, device fingerprints, etc.). Some High-risk operations may require upgrading to biological feature detection (fingerprints, face recognition).

For Web3, human-machine detection is also a strong demand.

For some project airdrops, cheaters can create multiple fake accounts to launch witch attacks. At this time, we need to identify the real person.

Due to the financial attributes of Web3, for some high-risk operations, such as account login, currency withdrawal, transactions, transfers, etc., it is not only the real person who needs to verify the user, but also the account owner, so face recognition is the best choice. select.

The demand is certain, but the question is how to realize it?

As we all know, decentralization is the original intention of Web3. When we discuss how to implement face recognition on Web3, the deeper question is actually how Web3 should adapt to AI scenarios:

• How should we build it? Centralized machine learning computing network?
• How to ensure that the privacy of user data is not leaked?
• How to maintain the operation of the network, etc.?

3. Privasea AI NetWork - Exploration of Privacy Computing + AI

Regarding the problems mentioned at the end of the previous chapter, Privasea gave a groundbreaking solution: Privasea built Privasea based on FHE (Fully Homomorphic Encryption) AI NetWork solves the privacy computing problem of AI scenarios on Web3.

FHE In layman’s terms, it is an encryption technology that ensures that the results of the same operation on plain text and cipher text are consistent.

Privasea has optimized and encapsulated the traditional THE, divided into application layer, optimization layer, arithmetic layer and original layer, forming the HESea library to adapt it to machine learning scenarios. The following are the specific functions responsible for each layer. :

Through its layered structure, Privasea provides more specific and tailored solutions to meet the unique needs of each user.

Privasea’s optimized packaging mainly focuses on the application layer and optimization layer. Compared with basic solutions in other homomorphic libraries, these customized calculations can provide more than a thousand times acceleration.

3.1 Network architecture of Privasea AI NetWork

Looking at the architecture of Privasea AI NetWork:

There are a total of 4 roles on its network, data owner, Privanetix node, decryptor, and result recipient.

• Data owner: Used to submit tasks and data securely through Privasea API.
• Privanetix Node: It is the core of the entire network. It is equipped with advanced HESea library and integrates a blockchain-based incentive mechanism to perform safe and efficient calculations while protecting the privacy of the underlying data and ensuring the integrity and confidentiality of calculations. .
• Decryptor: Obtain the decrypted result through Privasea API and verify the result.
• Result recipient: The task result will be returned to the person designated by the data owner and task issuer.

3.2 Core Workflow of Privasea AI NetWork

The following is the general workflow diagram of Privasea AI NetWork:

• STEP 1: User Registration: The data owner registers in Privacy by providing the necessary authentication and authorization credentials Start the registration process on the AI ​​network. This step ensures that only authorized users can access the system and participate in network activities.
• STEP 2: Task submission: Submit the calculation task and input data. The data is encrypted by the HEsea library. At the same time, the data owner also specifies authorized decryptors and result recipients who can access the final results.
• STEP 3: Task Allocation: Blockchain-based smart contracts deployed on the network allocate computing tasks to appropriate Privanetix nodes based on availability and capabilities. This dynamic allocation process ensures efficient resource allocation and distribution of computing tasks.
• STEP 4: Encrypted calculation: The designated Privanetix node receives the encrypted data and uses the HESea library to perform calculations. These calculations can be performed without decrypting sensitive data, thus maintaining its confidentiality. To further verify the integrity of the calculations, Privanetix nodes generate zero-knowledge proofs for these steps.
• STEP 5: Key Switching: After completing the calculation, the designated Privanetix node uses key switching technology to ensure that the final result is authorized and only accessible to the designated decryptor.
• STEP 6: Result Verification: After completing the calculation, the Privanetix node transmits the encrypted result and the corresponding zero-knowledge proof back to the blockchain-based smart contract for future verification.
• STEP 7: Incentive mechanism: Track the contribution of Privanetix nodes and distribute rewards
• STEP 8: Result retrieval: The decryptor utilizes the Privasea API to access the encrypted results. Their first priority is to verify the integrity of the calculations, ensuring that Privanetix nodes performed the calculations as intended by the data owner.
• STEP 9: Result Delivery: Share the decrypted results with designated result recipients pre-determined by the data owner.

In the core workflow of Privasea AI NetWork, what is exposed to users is an open API, which allows users to only pay attention to the input parameters and corresponding results without having to understand the complex operations within the network itself. There will not be too much Much mental burden. At the same time, end-to-end encryption prevents the data itself from being leaked without affecting data processing.

PoW && PoS dual mechanism superposition

Privasea’s recently launched WorkHeart NFT and StarFuel NFT use the dual mechanisms of PoW and PoS to manage network nodes and issue rewards. By purchasing WorkHeart NFT, you will be qualified to become a Privanetix node to participate in network computing and obtain token income based on the PoW mechanism. StarFuel NFT is a node gainer (limited to 5,000) that can be combined with WorkHeart. Similar to PoS, the more tokens pledged to it, the greater the revenue multiplier of the WorkHeart node.

So, why PoW and PoS?

In fact, this question is easier to answer.

The essence of PoW is to reduce the node evil rate and maintain the stability of the network through the time cost of calculation. Different from the large number of invalid calculations in BTC's random number verification, the actual work output (operation) of this privacy computing network node can be directly linked to the workload mechanism, which is naturally suitable for PoW.

And PoS makes it easier to balance economic resources.

In this way, WorkHeart NFT obtains income through the PoW mechanism, while StarFuel NFT increases the income multiple through the PoS mechanism, forming a multi-level and diversified incentive mechanism, allowing users to choose appropriate participation methods based on their own resources and strategies. The combination of the two mechanisms can optimize the revenue distribution structure and balance the importance of computing resources and economic resources in the network.

3.3 Summary

It can be seen that Privatosea AI NetWork has built an encrypted version of the machine learning system based on FHE. Thanks to the characteristics of FHE privacy computing, the computing tasks are subcontracted to various computing nodes (Privanetix) in a distributed environment, the validity of the results is verified through ZKP, and the dual mechanisms of PoW and PoS are used to provide computing results. Nodes reward or punish to maintain the operation of the network.

It can be said that the design of Privasea AI NetWork is paving the way for privacy-preserving AI applications in various fields.

4. FHE homomorphic encryption - the new holy grail of cryptography?

We can see in the last chapter that the security of Privatosea AI NetWork relies on its underlying FHE. With the continuous technological breakthroughs of ZAMA, the leader of the FHE track, FHE has even been dubbed the new Holy Grail of cryptography by investors. title, let’s compare it to ZKP and related solutions.

By comparison, it can be seen that the applicable scenarios of ZKP and FHE are quite different. FHE focuses on privacy calculation, while ZKP focuses on privacy verification.

SMC seems to have a greater overlap with FHE. The concept of SMC is secure joint computing, which solves the data privacy problem of individual computers that perform joint calculations.

5. Limitations of FHE

FHE achieves the separation of data processing rights and data ownership, thus preventing data leakage without affecting calculations. But at the same time, the sacrifice is computing speed.

Encryption is like a double-edged sword. While it improves security, it also greatly reduces the computing speed.

In recent years, various types of FHE performance improvement solutions have been proposed, some based on algorithm optimization and some relying on hardware acceleration.

• In terms of algorithm optimization, new FHE solutions such as CKKS and optimized bootstrap methods significantly reduce noise growth and computational overhead;
• In terms of hardware acceleration, customized GPU, FPGA and other hardware significantly improve the performance of polynomial operations.

In addition, the application of hybrid encryption schemes is also being explored. By combining partially homomorphic encryption (PHE) and search encryption (SE), efficiency can be improved in specific scenarios.

Despite this, FHE still has a large gap in performance from plaintext calculations.

6. Summary

Privasea not only provides users with a highly secure data processing environment through its unique architecture and relatively efficient privacy computing technology, but also opens a new chapter in the deep integration of Web3 and AI. Although the FHE it relies on at the bottom has a natural computing speed disadvantage, Privasea has recently reached a cooperation with ZAMA to jointly solve the problem of privacy computing. In the future, with continuous technological breakthroughs, Privasea is expected to unleash its potential in more fields and become an explorer of privacy computing and AI applications.

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