Hello everyone, I am Paul from Coinmanlabs. Today I would like to talk to you about an AI project - Privasea.

Q·What is a data island?

Many of us have experienced that when we go to the hospital for treatment, we need to bring films, medical records and other information. 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 be incompatible, resulting in doctors being unable to directly access and integrate patients' complete medical record information when they visit different medical institutions.

This is because inconsistent technical standards, strong independence of hospital management, privacy regulations and other restrictions may make it difficult to share and integrate medical data.

Similarly, many people have experienced the cumbersome process of going to different government departments to handle business. This is because different government departments and agencies are responsible for different public services and data collection. For example, the tax department, social security department, and health department each manage a large amount of data, but these data are usually not seamlessly integrated and shared, resulting in inefficient public services. Factors such as laws, privacy protection, and independent government structures limit the ability of government departments to share and integrate data.

This is one of the many examples we hear of data silos, which refers to the phenomenon where data cannot be effectively integrated and shared.

Data silos can exist for a variety of reasons:

1. Technical barriers: Different systems or platforms use different data formats, storage methods, interface standards, etc., which makes it difficult for data to be interoperable.

2. Organizational structure issues: There is a lack of effective data sharing mechanisms and culture between different departments or business units within large organizations, resulting in vertical or functional isolation of data.

3. Legal and privacy issues: The data involves sensitive information or is subject to laws and regulations, which may limit or hinder data sharing.

4. Data ownership and control: The owner or controller of the data is unwilling or unable to share the data with other entities, which may involve issues such as commercial interests and competitive relationships.

5. Cost and resource limitations: Data integration and sharing may require a lot of resources and costs, and some organizations may not be able or willing to invest these resources.

6. Culture and ideology: Some organizations or individuals may believe that data should be private and are unwilling or uncomfortable sharing data with other parties.

Q·What are the common technical methods to solve data silos?

The current technical means of solving data silos in research and practice are mainly: Federated Learning, Zero-Knowledge Proofs (ZKP) and Fully Homomorphic Encryption (FHE), Secure Multiparty Computation (SMC), Differential Privacy, and Split Learning.

Due to space constraints, we will not discuss each one in detail today, but will mainly talk about Fully Homomorphic Encryption (FHE).

FHE

First of all, let's think about what is the most important word in fully homomorphic encryption? I think it must be homomorphism. Indeed, homomorphism is the core of fully homomorphic encryption technology. It enables complex calculations and operations on data in an encrypted state, providing a powerful solution for data security and privacy protection.

Homomorphism is a concept in mathematics, specifically referring to the mapping between two sets (usually the same set) in an algebraic structure, which maintains the structure of the operation. In Fully Homomorphic Encryption (FHE), homomorphism is one of its core features, which allows complex calculations to be performed in an encrypted state without decrypting the data.

In fully homomorphic encryption, two main types of homomorphism are usually involved: additive homomorphism and multiplicative homomorphism.

Let's define fully homomorphic encryption. Fully homomorphic encryption (FHE) is a special encryption technology that allows arbitrary calculations to be performed in an encrypted state, and the results obtained can be decrypted to be exactly the same as the calculation results of unencrypted data. This feature allows complex calculations and data processing to be performed while keeping the data encrypted without having to decrypt the data.

Basic principle: The basic concept of FHE is to implement it through a series of mathematical operations, including addition and multiplication operations. The encryption algorithm of FHE allows the encrypted data to be added and multiplied in the encryption domain without decryption to get the final result. FHE schemes are usually based on public key cryptography, using public keys for encryption and private keys for decryption, while ensuring the confidentiality and integrity of the calculations.

At present, the main application scenarios of FHE are: Secure computing outsourcing: allowing data to be sent to cloud service providers without decryption so that calculations can be performed in an encrypted state. Privacy-preserving data analysis: allowing data owners to analyze and process data while keeping the data encrypted, such as medical data analysis, financial data analysis, etc.

So why can’t it be used on a large scale now?

Computational efficiency: The encryption and decryption process of FHE is usually time-consuming, especially for complex cryptographic operations.

Key management: Securely managing public and private keys is critical to the implementation of FHE, and issues such as key generation, distribution, and update need to be considered.

Security assurance: Although FHE provides strong cryptographic capabilities, the security and vulnerabilities of the implementation need to be carefully considered in practical applications.

So can we process the data without exposing the original information form? Sensitive information can be processed without exposing the original form to ensure the confidentiality of sensitive information.

Privacy

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 uses an innovative technology called fully homomorphic encryption (FHE), which can perform calculations on encrypted data and produce the same results as calculations performed on unencrypted data. It realizes the circulation 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 incentive crowdsourcing of $PRVA tokens.

Investment agency:

system structure

The Privasea AI Network consists of four main components: HESea Library, Privasea API, Privanetix, and Privasea Smart Contract Suite.

At the core of the Privasea AI network is the HESea library, which has efficient implementations of a large number of popular fully homomorphic encryption schemes such as TFHE, CKKS, BGV, BFV, etc.

This open source library provides developers with cryptographic techniques and high-performance optimizations for secure computing. With the HESea library, developers can access a variety of functions to perform basic primitive, arithmetic, and logical operations on encrypted data. The library is unique in that it has been carefully optimized, 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. This API is a valuable resource for developers looking to build privacy-preserving AI applications.

By leveraging the power of the underlying FHE scheme provided by the HESea library, developers can create powerful applications that prioritize data privacy and security. The Privasea API enables developers to seamlessly integrate advanced privacy-preserving features into their AI applications.

Privanetix is ​​a network of interconnected computing nodes whose mission is to enable secure computation on encrypted data. These nodes perform computations on encrypted data using the FHE algorithm, ensuring that sensitive information cannot be discovered by criminals.

Privanetix enhances the scalability and efficiency of the Privasea AI network by distributing computations across multiple nodes. The network acts as a strong shield to prevent data breaches and unauthorized access, further enhancing the security of users’ sensitive information.

In order to effectively manage the Privanetix network and incentivize computing nodes, the Privasea Smart Contract Suite was created. The suite includes a series of carefully designed smart contracts to handle various aspects of network management. By using these smart contracts, organizations can effectively manage the Privanetix network and ensure that everything runs smoothly. In addition, the Privasea Smart Contract Suite provides incentives for computing nodes to encourage them to actively participate, further enhancing the overall performance of the network.

Sign up for ImHuman

Currently, the official website also states that you can get airdrops by registering for ImHuman, and the first season of the Genesis event is in progress: user increase. Then we can try to get started,

Precautions

Season 1 event time: May 27th - July 31st

Multi-level invitation:

Genesis Code: Users with Genesis Code have 3 levels of recommendation power.

Level 1 (Direct Referral): Earn 100 stars for each user you refer.

Level 2 (Recommendations from people you refer): You earn 50 stars for each user you refer.

Level 3 (referrals from your Level 2 referrals): Earn 25 stars for each user you refer.

Derivative Code: Users with Derivative Code have Level 2 referral power.

Level 1 (Direct Referral): Earn 100 stars for each user you refer.

Level 2 (Recommendations from people you refer): You earn 50 stars for each user you refer.

At the end of the season, stars can be redeemed 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 phone.

If you don't have the Google Store, you can click to directly download the Android APK to install locally.

STEP.2Register an account

After downloading the account, you can register the account.

Just fill in the invitation code: cLz7aZS.

STEP.3mint your own NFT

Because stars are closely related to our subsequent airdrops, we recommend that you get more stars. This mainly involves minting an NFT, which costs 0.03sol (about 4U)

We click Crypto to get our own sol address, enter a certain amount of sol into the address, and then click NFT to mint the specified NFT. When you are done, you can get the corresponding stars.

think

  • This project has been invested by both Binance and OKX, and it is worth our efforts.

  • With the rise of technologies such as zkp, more people will pay attention to the FHE track, and we need to keep an eye on it.

  • Currently, there is a certain threshold for facial recognition.