深入解讀Privasea：人臉資料鑄造NFT，很有趣的創新？

1.引言

近日，一個由 Privasea 發起的人臉NFT鑄造項目異常火爆！

乍看之下很簡單，在專案中使用者可以在IMHUMAN（我是人類）行動應用程式上錄入自己的人臉，並把自己的人臉資料鑄造為一枚NFT，就僅僅是這人臉資料上鏈+ NFT 的組合使得該項目在4月底上線以來獲得了超過20W+的NFT的鑄造量，熱度可見一斑。

我也很疑惑了，為什麼呢？人臉數據有多大也能上鍊嗎？我的人臉資料會被盜用嗎？ Privasea又是幹啥的？

等等，讓我們繼續對專案本身及專案方 Privasea 進行了研究，一探究竟。

關鍵字：NFT、AI、FHE（全同態加密）、DePIN

2、從Web2到Web3-人機對抗從未停止

首先，我們解讀一下人臉NFT鑄造這個項目本身的目的，如果你覺得這個計畫就是單純的把人臉資料鑄造成NFT那就大錯特錯了。

上文我們提到的這個項目的App名稱 IMHUMAN（我是人類） 已經很好的說明了這個問題：事實上，該項目旨在通過人臉識別來判斷屏幕前的你是否是真人。

首先，我們為什麼需要人機辨識？

根據Akamai提供的2024Q1報告（見附錄）顯示，Bot（一種自動化程序，可以模擬人發送HTTP請求等操作）驚人的佔據了互聯網流量的42.1%，其中惡意流量佔據了整個互聯網流量的27.5 %。

惡意的Bot可能會對中心化的服務商帶來延遲回應甚至是宕機等災難性的後果，影響真實使用者的使用體驗。

我們以搶票場景為例，作弊者透過新建多個虛擬帳號進行搶票操作，便可大幅提高搶票成功的機率，更有甚者直接把自動化程式部署在服務商的機房旁邊，實現幾乎0延時的購票。

普通用戶面對這些高科技用戶幾乎是毫無勝算可言。

服務商對此也做出了一些努力，對客戶端，Web2場景下透過引入實名認證、行為驗證碼等多種方式來區分人機，服務端則是透過WAF策略等手段進行特徵過濾攔截。

那這樣問題就能解決了嗎？

顯然沒有，因為作弊帶來的收益是豐厚的。

同時，人機的對抗具有持續性，作弊者與檢驗者兩個角色都在不斷升級自己的武器庫。

以作弊者為例，趁著近些年AI迅速發展的東風，客戶端的行為驗證碼幾乎被各種視覺類模型給降維打擊，甚至AI有著比人更快更準的識別能力。這使得校驗者不得不被動升級，由早期的用戶的行為特徵檢測（圖像類驗證碼）測逐漸過度到仿生物學特徵檢測（感知驗證：如客戶端環境監測、設備指紋等），一些高風險操作，可能需要上升到生物學特徵檢測（指紋、人臉辨識）。

對於Web3，人機偵測同樣是一個強需求。

對於某些項目空投，作弊者可以創建多個虛假帳號發動女巫攻擊，這時候我們需要辨別真人。

由於Web3的金融屬性，對於一些高風險操作，如帳號登入、提幣、交易、轉帳等，需要核實用戶的不僅僅是真人，並且是帳號所有者，人臉識別便成了不二之選。

需求是確定的，問題又是怎麼實現？

眾所周知，去中心化是Web3的初衷，當我們在討論如何在Web3上實現人臉辨識的時候，實際上更深層的問題是Web3應該怎麼適配AI場景：

• 我們應該如何搭建去中心化的機器學習計算網路？
• 怎麼保證用戶資料的隱私不會洩漏？
• 怎麼維護網路的運作等等？

3、Privasea AI NetWork-隱私計算+AI的探索

對於上一章文末提到的問題，而 Privasea 給出了開創性的解決方案：Privasea 基於FHE（全同態加密）構建了Privasea AI NetWork 來解決Web3上AI場景的隱私運算問題。

FHE 通俗講就是一種保證明文與密文進行相同運算後結果一致的加密技術。

Privasea 對傳統的THE進行了優化封裝，劃分了應用層、優化層、算術層和原始層，形成了HESea庫，使其適配了機器學習場景，以下是具體每一層的負責的功能：

透過分層結構，Privasea 提供了更具體和量身定制的解決方案，以滿足每個用戶的獨特需求。

Privasea 的最佳化封裝主要集中在應用層和最佳化層，與其他同態庫中的基本解決方案相比，這些客製化計算可以提供超過千倍的加速。

3.1 Privasea AI NetWork的網路架構

從其Privasea AI NetWork的架構看：

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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