MT Capital Report: Will Privasea Bring Fully Homomorphic Encryption to Mass Adoption?

Author: Xinwei, MT Capital

MT Capital has always been committed to investing in innovative enterprises with disruptive technological potential. We believe that the combination of fully homomorphic encryption (FHE) and AI in the decentralized physical infrastructure network (DePIN) is a crucial track for the future. FHE technology can perform calculations while keeping data encrypted, ensuring privacy and security throughout the data processing process. The integration of AI and DePIN can efficiently utilize external computing resources and conduct complex data analysis and machine learning tasks without worrying about data leakage. Privasea’s leading position and technological advantages in this field are highly aligned with MT Capital’s investment strategy. We believe that by supporting Privasea, we will promote the development of the FHE AI DePIN track, and contribute to the security and sustainable development of the global digital economy.

1. What is fully homomorphic encryption (FHE)?

Fully Homomorphic Encryption (FHE) is an encryption technology that allows arithmetic or logical operations to be performed directly on ciphertext while maintaining the encrypted state of the data. This means that complex operations can be performed on encrypted data without the need to decrypt it into plaintext, which is revolutionary in terms of maintaining data privacy and security.

In traditional data processing scenarios, in order to perform calculations, the data must first be decrypted, exposing sensitive information and increasing the risk of data theft or misuse. The application of FHE technology has completely changed this. With FHE, encrypted data can be directly input into the calculation process, and the calculation results remain encrypted until decryption is required to view the results. This feature is very important for industries that need to handle sensitive data, such as finance, healthcare, and government departments.

FHE also enables the possibility of outsourcing data processing without sacrificing data confidentiality. Companies can send encrypted data to third-party service providers for complex data analysis or machine learning tasks, without worrying about data leakage, as the service providers cannot see the original data throughout the process.

2.Privasea: The first AI+DePIN network that uses FHE

Privasea uses FHE technology to provide data privacy and security, utilizing AI and distributed network architecture to allow for complex data processing and analysis while keeping the data completely encrypted. This means that users can perform machine learning and other advanced computations without exposing the raw data, which is impossible in traditional cloud computing, disrupting privacy computing.

The Privasea platform has adopted several advanced FHE schemes, such as TFHE and CKKS, all of which provide highly effective data privacy protection while ensuring computational accuracy and efficiency. Among them, the TFHE scheme supports fast bit operations within a single instruction cycle, while the CKKS scheme optimizes the ability to handle floating-point numbers, enabling Privasea to effectively support various complex scientific and commercial applications, such as financial analysis, medical data processing, and machine learning tasks.

In addition, Privasea has also implemented a highly scalable distributed computing network, Privanetix. This network consists of multiple computing nodes, each of which can perform FHE operations, providing the necessary computing resources. This distributed architecture not only enhances the platform’s processing power, but also improves the redundancy and fault tolerance of the system, ensuring high availability and reliability of the service. The integration of AI with a distributed network allows Privasea to handle advanced AI tasks such as deep learning, pattern recognition, and machine learning, which typically require massive computing power and high-level data protection. For example, users in the healthcare industry can use Privasea to securely analyze sensitive patient data, conduct disease prediction, and optimize treatment plans without worrying about violating data protection regulations.

Privasea also offers a unique smart contract suite that allows users to manage and automate data processing workflows, including data validation, result output, and task allocation and rewards, while keeping the data encrypted. These smart contracts are executed on a distributed ledger, ensuring not only process transparency and traceability, but also automated incentive allocation based on the computing resources provided by nodes. This blockchain-based incentive mechanism further enhances network participation and computing efficiency, as nodes are motivated to provide reliable services. This makes Privasea not only a data encryption and processing platform, but also a complete encrypted data ecosystem.

Developers can easily access this complex system and utilize its powerful features to develop and deploy their own AI applications through Privasea’s API. These applications can utilize distributed networks to distribute computing loads, while ensuring the integrity and security of the data, which is particularly important for blockchain applications that need to process large amounts of sensitive data.

3. Cooperation with Solana showcases the potential of Mass Adoption

Privasea uses FHE technology to introduce the ImHuman application, which not only demonstrates the application of FHE in countering Sybil attacks, but also signifies its mass adoption potential in the encryption field. Sybil attacks are a major threat in decentralized networks, especially in the airdrop field, where attackers manipulate the network or gain unfair advantages by creating a large number of false identities. The ImHuman application effectively combats such attacks in a secure and privacy-protecting manner.

The Privasea project plans to deploy its technology on the Solana network, becoming the first Proof of Human application on Solana. Solana’s high performance and low latency make it an ideal blockchain platform to support Privasea’s FHE technology and AI computing needs. This deployment will not only enhance the security of the Solana ecosystem but also demonstrate the potential of FHE in Web3 applications. By running on Solana, Privasea’s ImHuman application can verify user identities more widely, ensuring network security and reliability while protecting user privacy.

The working principle of the ImHuman application is to use the user’s biometric data to create a unique digital identity. First, the user needs to scan their facial vector through the front camera of the application, which is completely done on the user’s device to ensure that sensitive data is not leaked. Then, these data are encrypted and transformed into an NFT representing the user’s encrypted biometric vector. This uses the property of FHE to perform complex calculations without decrypting the data, ensuring data security and privacy.

During user authentication, the ImHuman application rescans the user’s facial features and compares the newly collected data with the encrypted data stored on the blockchain. This process also uses FHE technology to ensure that the data will not be decrypted during the verification process, effectively avoiding the risk of data leakage. In addition, since each user’s NFT is generated based on their unique biological characteristics, it is difficult to replicate or forge, greatly increasing the difficulty of conducting a Sybil Attack.

Through the ImHuman application, Privasea not only provides a powerful tool to enhance the security of decentralized networks, but also demonstrates the feasibility of fully homomorphic encryption technology in real-world applications. This biometrics and FHE-based authentication method provides a secure and privacy-preserving solution for decentralized networks, making Privasea’s ImHuman the first application with Mass Adoption potential in the FHE field. In addition, by distributing airdrop rewards to participants, ImHuman can also incentivize user participation and continuous usage, further promoting its widespread adoption. This innovative solution provides a new defense strategy against Sybil Attacks.

4. Comparison between Privasea and Existing Proof of Human Solutions

In the current Proof of Human schemes, projects like Worldcoin and Human Protocol face compliance risks and privacy issues. Taking Worldcoin as an example, the recent investigation conducted by the Office of the Privacy Commissioner for Personal Data in Hong Kong found that Worldcoin’s operations in Hong Kong violated the Personal Data (Privacy) Ordinance. The investigation revealed that individuals participating in the Worldcoin project were required to collect facial and iris images through iris scanning to verify their human identity, which poses significant risks to personal data privacy. Therefore, the Privacy Commissioner in Hong Kong has requested Worldcoin to cease collecting the iris and facial images of citizens in Hong Kong.

Human Protocol verifies by collecting user task response data, interaction data, device and browser information, geographic location, and user behavior data. Although these data are anonymized and encrypted before use, there is still a significant amount of personal data collection, which poses certain privacy and compliance risks.

In contrast, Privasea places more emphasis on user privacy protection in its design. Privasea’s DApp “ImHuman” uses FHE technology for user identity verification, without the need to collect sensitive information such as facial or iris images. The verification process is entirely conducted on the user’s mobile device, and the facial vector data is encrypted and not transmitted to any server. In this way, Privasea not only ensures verification security, but also maximizes user privacy protection and avoids the risk of data leakage.

Privasea not only leads in privacy protection, but also provides powerful data privacy and security solutions through the integration of FHE, DePIN, and ZK technologies. These technologies allow Privasea to perform complex data processing and analysis without exposing user data, further reducing compliance risks. This unparalleled privacy protection and data security capability sets Privasea apart in the competition and makes it a leading Proof of Human solution in the industry.

5. Accseal and Privasea join hands to deepen privacy computing

Privasea has set a new standard in the field of privacy computing with its outstanding FHE, DePIN, and ZK technology capabilities. As a pioneer in the AI DePIN field, Privasea seamlessly integrates distributed computing networks with advanced security measures through its innovative Fully Homomorphic Encryption Machine Learning (FHEML) solution, establishing a new benchmark for data privacy and security. The DApp introduced by Privasea, “ImHuman,” securely executes “Proof of Humanity” (PoH) using FHE technology to directly encrypt facial vector data on users’ mobile devices, without transmission through servers, greatly enhancing privacy protection and the security of user data.

In this context, Privasea has reached a strategic cooperation with Accseal to further enhance its technological advantages. As a leading enterprise in privacy computing hardware acceleration, Accseal will provide hardware acceleration support to Privasea, improving the efficiency and performance of its FHE operations. The two parties will jointly explore the possibilities of integrating ZK and FHE technologies, aiming to improve the efficiency of privacy computing and expand its application scope.

Through this cooperation, Privasea not only demonstrates its leadership position in the field of FHE, but also elevates its DePIN project to new heights. Accseal will develop new hardware acceleration products to provide computational acceleration support for upper-layer applications like Privasea, further driving the development of privacy computing technology. The cooperation between the two parties indicates that the field of privacy computing will usher in new breakthroughs, especially in the wider and deeper application of the DePIN project.