What is Privasea, which focuses on the dual narrative of FHE+AI, with investments from Binance and OKX? (Includes interactive tutorial)
Privasea AI Network is a powerful system designed to prioritize data privacy and security throughout the entire AI computing process.Author: Paul, CoinmanLabs
Hello everyone, I am 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 images, medical records, and other information when visiting a hospital. Have you ever wondered why?
In the medical field, different hospitals and clinics may use different electronic medical record systems and databases. The data formats and interfaces between these systems may not be compatible, making it difficult for doctors to access and integrate a patient's complete medical history when they visit different medical institutions.
This is due to inconsistent technical standards, strong independence of hospital management, privacy regulations, and other restrictions that can make it difficult to share and integrate medical data.
Similarly, many people have experienced the cumbersome process of dealing with different government departments, requiring visits to various departments. This is because different government departments and agencies are responsible for different public services and data collection. For example, tax departments, social security departments, and health departments each manage large amounts of data, but this data often cannot be seamlessly integrated and shared, leading to inefficiencies in public services. Legal, privacy protection, and government structural independence factors limit the ability of government departments to share and integrate data.
These are several examples of what we refer to as data islands, which refer to the phenomenon where data cannot be effectively integrated and shared.
There are various reasons for the existence of data islands:
Technical barriers: Different systems or platforms use different data formats, storage methods, and interface standards, making it difficult for data to interoperate.
Organizational structure issues: Large organizations may 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 may involve sensitive information or be restricted by laws and regulations, limiting or obstructing data sharing.
Data ownership and control: Data owners or controllers may be unwilling or unable to share data with other entities, potentially involving commercial interests and competitive relationships.
Cost and resource constraints: Data integration and sharing may require significant resources and costs, which some organizations may not be able or willing to invest.
Cultural and ideological factors: Some organizations or individuals may believe that data should be private and may be unwilling or unaccustomed to sharing data with others.
Q·What are common technical means to solve data islands?
Current research and practical solutions to data islands mainly include: Federated Learning, Zero-Knowledge Proofs (ZKP), Fully Homomorphic Encryption (FHE), Secure Multiparty Computation (SMC), Differential Privacy, and Split Learning.
Due to space constraints, we will not elaborate on each one today, but will mainly discuss Fully Homomorphic Encryption (FHE).
FHE
First, let's think about what the most critical term in Fully Homomorphic Encryption is. It must be homomorphism, right? Indeed, homomorphism is the core of fully homomorphic encryption technology, allowing complex computations and operations to be performed on data while it is in an encrypted state, providing a powerful solution for data security and privacy protection.
Homomorphism is a mathematical concept that refers to a mapping between two sets (usually the same set) in an algebraic structure that preserves the structure of operations. In Fully Homomorphic Encryption (FHE), homomorphism is one of its core features, enabling complex computations to be performed in an encrypted state without needing to decrypt the data.
In Fully Homomorphic Encryption, two main types of homomorphism are typically involved: additive homomorphism and multiplicative homomorphism.
Let's define Fully Homomorphic Encryption (FHE): it is a special encryption technology that allows arbitrary computations to be performed in an encrypted state, with the results being identical to those obtained from computations performed on unencrypted data after decryption. This feature allows complex computations and data processing to be carried out while keeping the data encrypted, without the need to decrypt it.
Basic principle: The fundamental 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 within the encrypted domain, obtaining the final result without decryption. FHE schemes are typically based on public-key cryptography, using a public key for encryption and a private key for decryption, while ensuring the confidentiality and integrity of the computations.
Currently, the main application scenarios for FHE include: secure computation outsourcing, allowing data to be sent to cloud service providers for computation while remaining encrypted; privacy-preserving data analysis, allowing data owners to analyze and process data while keeping it encrypted, such as in 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 typically 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 updating.
Security guarantees: Although FHE provides powerful encryption capabilities, careful consideration of the security and vulnerabilities of the implementation is needed in practical applications.
So can we process data without exposing the original form of the information? Sensitive information can be processed without exposing its original form, ensuring the confidentiality of sensitive information.
Privasea
Website: https://www.privasea.ai/
Twitter: https://x.com/Privasea_ai
Introduction: The Privasea AI network is a powerful system designed to prioritize data privacy and security throughout the AI computing process. It employs an innovative technology called Fully Homomorphic Encryption (FHE), which allows computations to be performed on encrypted data, yielding results identical to those obtained from computations on unencrypted data. It facilitates the flow of data value through FHEML. The network provides distributed computing resources for FHE AI operations. The entire system is supported by ZAMA's specific ML and the $PRVA token incentive crowdsourcing.
Investment institutions:
System Architecture
The Privasea AI network consists of four main components: the HESea library, the Privasea API, Privanetix, and the Privasea smart contract suite.
The core of the Privasea AI network is the HESea library, which features efficient implementations of many popular fully homomorphic encryption schemes, such as TFHE, CKKS, BGV, and BFV.
This open-source library provides developers with encryption technology and high-performance optimizations for 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 optimizations, employing techniques such as ciphertext packing and batching to enhance efficiency and overall performance.
The Privasea API is a comprehensive set of protocols and tools built on top of the HESea library. This API is a valuable resource for developers looking to build privacy-preserving AI applications.
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 a network of interconnected computing nodes tasked with enabling secure computations on encrypted data. These nodes utilize FHE algorithms to compute on encrypted data, ensuring that sensitive information remains undisclosed to unauthorized parties.
Privanetix enhances the scalability and efficiency of the Privasea AI network by distributing computations across multiple nodes. The network acts as a powerful shield against 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 utilizing these smart contracts, organizations can effectively manage the Privanetix network and ensure smooth operations. Additionally, the Privasea smart contract suite provides incentives for computing nodes, encouraging their active participation and further enhancing the overall performance of the network.
Register ImHuman
Currently, the official website has stated that registering for ImHuman can earn airdrops, and the first season of the Genesis event is underway: user growth. We can try to get involved.
Notes
First season event duration: May 27 - July 31
Multi-level invitations:
Genesis code: Users with a Genesis code have 3 levels of referral power.
Level 1 (direct referral): Earn 100 stars for each user referred.
Level 2 (referrals of your referrals): Earn 50 stars for each user referred by your referral.
Level 3 (referrals of your level 2 referrals): Earn 25 stars for each user referred by your level 2 referral.
Derivative code: Users with a derivative code have 2 levels of referral power.
Level 1 (direct referral): Earn 100 stars for each user referred.
Level 2 (referrals of your referrals): Earn 50 stars for each user referred by your referral.
At the end of the season, stars can be exchanged for Privasea official airdrops.
STEP.1 Download ImHuman
We can go to https://www.privasea.ai/download-app to download the corresponding app to our mobile phones.
If you do not have the Google Play Store, you can click to directly download the Android APK for local installation.
STEP.2 Register an account
Once the app is downloaded, you can proceed to register an account.
Fill in the invitation code: cLz7aZS.
STEP.3 Mint your own NFT
Since stars are closely related to our future airdrops, it is recommended to collect more stars. This mainly involves minting an NFT, which costs around 0.03 sol (approximately $4).
Click on Crypto to obtain your sol address, transfer the required amount of sol to that address, and then click on NFT to mint the specified NFT. Once completed, you will receive the corresponding stars.
Thoughts
- This project has received investments from both Binance and OKX, making it worth pursuing.
- With the rise of technologies like ZKP, more people will pay attention to the FHE track, and we need to stay vigilant.
- Currently, facial recognition has certain barriers to entry.
