Hello everyone, I'm Paul from Coinmanlabs, and today I want to talk to you about an AI project - Privasea.
Q·What is a data island?
Many of us have experienced the need to bring films, medical records, and other information when going to the hospital. Have you ever wondered why?
In the medical field, different hospitals and clinics may use different electronic medical record systems and databases. The incompatibility of data formats and interfaces between these systems may prevent doctors from directly accessing and integrating their complete medical records when patients seek treatment at different medical institutions.
This is due to inconsistent technical standards, strong independence in hospital management, privacy regulations, and other factors, all of which may make it difficult to share and integrate medical data.
Similarly, many people have experienced the hassle of dealing with different government departments for various services, as different departments and agencies are responsible for different public services and data collection. For example, tax departments, social security departments, and health departments each manage a large amount of data, but these data are usually unable to be seamlessly integrated and shared, leading to inefficiencies in public services. Factors such as laws, privacy protection, and the independence of government structures limit the ability of government departments to share and integrate data.
These are multiple examples of what we refer to as data islands, which are situations where data cannot be effectively integrated and shared.
There can be various reasons for the existence of data islands:
Technical barriers: Different systems or platforms use different data formats, storage methods, interface standards, etc., making it difficult for data to be interoperable.
Organizational structure issues: Large organizations lack effective data sharing mechanisms and culture between different departments or business units, leading to vertical or functional isolation of data.
Legal and privacy issues: Data involving sensitive information or subject to legal regulations may restrict data sharing.
Data ownership and control: Data owners or controllers may be unwilling or unable to share data with other entities, possibly due to business interests, competitive relationships, etc.
Cost and resource constraints: Data integration and sharing may require a large amount of resources and costs, which some organizations may be unable or unwilling to invest in.
Culture and ideology: Some organizations or individuals may believe that data should be private and may be unwilling or unaccustomed to sharing data with others.
Q·Common technical means to solve data islands?
The current main technical means for addressing data islands are: Federated Learning, Zero-Knowledge Proofs (ZKP), Fully Homomorphic Encryption (FHE), Secure Multiparty Computation (SMC), Differential Privacy, and Split Learning.
Due to space constraints today, we will not go into detail about each one, but we will mainly discuss Fully Homomorphic Encryption (FHE).
FHE
First, let's think about the most crucial word in Fully Homomorphic Encryption (FHE). I believe it must be "homomorphic," and indeed it is. Homomorphism is the core of fully homomorphic encryption technology, allowing data to undergo complex calculations and operations while in an encrypted state, providing a powerful solution for data security and privacy protection.
Homomorphism is a mathematical concept that specifically refers to a mapping between two sets (usually the same set) in algebraic structures, preserving the structure of operations. In Fully Homomorphic Encryption (FHE), homomorphism is one of its core features, allowing complex calculations to be performed in an encrypted state without the need to decrypt the data.
In Fully Homomorphic Encryption, there are typically two main types of homomorphism: additive homomorphism and multiplicative homomorphism.
Let's give Fully Homomorphic Encryption a definition. Fully Homomorphic Encryption (FHE) is a special encryption technology that allows for arbitrary calculations in an encrypted state, with the results being identical to those of calculations performed on unencrypted data. This feature allows data to undergo complex calculations and data processing while remaining encrypted, without the need to decrypt the data.
Basic principle: The basic concept of FHE is achieved through a series of mathematical operations, including addition and multiplication. The encryption algorithm of FHE allows encrypted data to undergo addition and multiplication operations in the encrypted domain, without the need to decrypt to obtain the final result. FHE schemes are typically built on the basis of public key cryptography, using public keys for encryption and private keys for decryption, while ensuring the confidentiality and integrity of the calculations.
Currently, the main applications of FHE are: Secure computation outsourcing, allowing data to be sent to cloud service providers without decryption for calculations in an encrypted state. Privacy-protected data analysis, allowing data owners to perform data analysis and processing while keeping the data encrypted, such as medical data analysis and financial data analysis.
So why can't it be widely used at present?
Computational efficiency: The encryption and decryption processes of FHE are usually time-consuming, especially for complex encryption operations.
Key management: Securely managing public and private keys is crucial for the implementation of FHE, requiring consideration of key generation, distribution, and updates.
Security assurance: Although FHE provides powerful encryption capabilities, the security and vulnerabilities of the implementation need to be carefully considered in practical applications.
So can we process data without exposing the original information? Sensitive information can be processed without exposing the original form, ensuring the confidentiality of sensitive information.
Privasea
Website: https://www.privasea.ai/
Twitter: https://x.com/Privasea_ai
Introduction: Privasea AI Network is a powerful system designed to prioritize the privacy and security of data throughout the AI computing process. It uses an innovative technology called Fully Homomorphic Encryption (FHE), which allows for calculations on encrypted data to produce the same results as calculations on unencrypted data. It enables the circulation of data value through FHEML. The network provides distributed computing resources for FHE AI operations. The entire system is supported by specific ML from ZAMA and the incentive crowdsourcing of the $PRVA token.
Investment institutions:
System Architecture
The Privasea AI Network consists of four main components: HESea Library, Privasea API, Privanetix, and Privasea Smart Contract Suite.
The core of the Privasea AI Network is the HESea Library, which has efficient implementations of popular fully homomorphic encryption schemes such as TFHE, CKKS, BGV, BFV, etc.
This open-source library provides developers with encryption technology and high-performance optimization to achieve secure computation. With the HESea library, developers can access various functions to perform basic primitive, arithmetic, and logical operations on encrypted data. The uniqueness of this library lies in its meticulous optimization, using techniques such as ciphertext packing and batching to improve efficiency and overall performance.
The Privasea API is a comprehensive set of protocols and tools built on top of the HESea library. For developers looking to build privacy-protecting AI applications, this API is a valuable resource.
By leveraging the powerful capabilities of the underlying FHE schemes provided by the HESea library, developers can create robust applications that prioritize data privacy and security. The Privasea API enables developers to seamlessly integrate advanced privacy protection features into their AI applications.
Privanetix is an interconnected computing node network tasked with securely computing on encrypted data. These nodes use FHE algorithms to compute on encrypted data, ensuring that sensitive information remains hidden from malicious actors.
By distributing computations across multiple nodes, Privanetix enhances the scalability and efficiency of the Privasea AI network. The network acts as a powerful shield, preventing data leaks and unauthorized access, further enhancing the security of users' sensitive information.
To effectively manage the Privanetix network and incentivize computing nodes, the Privasea Smart Contract Suite has been developed. This suite includes a series of carefully designed smart contracts to handle various aspects of network management. By using these smart contracts, organizations can efficiently manage the Privanetix network and ensure smooth operations. Additionally, the Privasea Smart Contract Suite provides incentives to computing nodes, encouraging active participation and further enhancing the overall performance of the network.
Register for ImHuman
Currently, the official website also states that registering for ImHuman can receive airdrops, and the first season of the Genesis activity is ongoing: user growth. We can try to participate in it.
Notes
Season 1 activity period: May 27th - July 31st
Multi-level invitation:
Genesis code: Users with the Genesis code have 3 levels of referral power.
Level 1 (direct referral): Receive 100 stars for each referred user.
Level 2 (referral of your referrals): Receive 50 stars for each referred user.
Level 3 (referral of your level 2 referrals): Receive 25 stars for each referred user.
Derivative code: Users with the derivative code have 2 levels of referral power.
Level 1 (direct referral): Receive 100 stars for each referred user.
Level 2 (referral of your referrals): Receive 50 stars for each referred user.
At the end of the season, stars can be exchanged for official airdrops from Privasea.
STEP.1 Download ImHuman
We can go to https://www.privasea.ai/download-app to download the corresponding app to our mobile phone.
If you don't have the Google Play Store, you can click to directly download the Android APK to install it locally.
STEP.2 Register an Account
After downloading the app, you can proceed with the account registration.
Enter the invitation code: cLz7aZS.
STEP.3 Mint Your Own NFT
Because stars are closely related to our future airdrops, it is recommended for everyone to collect more stars. Here, you mainly need to mint an NFT, which costs about 0.03 SOL (approximately 4U).
Click on Crypto to get your SOL address, deposit the specified amount of SOL to that address, and then click on NFT to mint the specified NFT. Once you complete this, you will receive the corresponding stars.
Considerations
- This project has received investments from Binance and OKX, making it worth our while to participate.
- With the rise of technologies like ZKP, more people will pay attention to the FHE track, so we need to keep a close eye on it.
- Currently, there is a certain threshold for facial recognition.
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