Privasea AI Network Leads the Way by Redefining Data Security and Privacy with FHE Tech

In the wake of ChatGPT's debut, a plethora of artificial intelligence (AI) tools have emerged, each offering innovative solutions.

Yet, this surge has also brought to light a critical concern: data privacy.

The training of AI models necessitates vast amounts of data, which inherently poses significant risks.

It is at this juncture that Privasea steps in.

What is Privasea and its Aim?

The Privasea AI Network is a robust framework crafted to prioritise the privacy and security of data during AI computations.

At its core, the network utilises Fully Homomorphic Encryption (FHE), a technology that allows for computations on encrypted data, yielding outcomes indistinguishable from those obtained using unencrypted data.

This approach ensures that sensitive information is processed without ever being revealed in its unencrypted form.

Leveraging FHE, the Privasea AI Network enables secure collaborative AI processing among various entities while maintaining the confidentiality of sensitive information.

Privasea's whitepaper highlights its capability to enable collaboration among multiple parties while safeguarding sensitive information from exposure:

“As the amount of data being generated and the number of users accessing data-driven applications increase, there are concerns about privacy protection and the lack of computational power. One potential solution to these issues is AI computation networks, which can offer efficient methods to stimulate computation power and maintain privacy throughout the data processing cycle."

Its client-server architecture redefines privacy and technology as seen below:

Below is the proof of humanness by FHE.

It consists of four main components: the HESea Library, the Privasea API, Privanetix and the Privasea Smart Contract Kit.

The whitepaper stated:

“To facilitate the implementation of FHE, Privasea incorporates a basic FHE library called HESea. This library equips developers with essential tools and functions to securely perform computations on encrypted data, such as addition, multiplication, and even evaluation of machine learning models. HESea empowers users to unlock the potential of their data without compromising privacy."

Privasea AI Network Architecture

The network's commitment to data protection extends to adherence with stringent regulations such as the European Union's General Data Protection Regulation (GDPR).

By employing privacy-preserving AI techniques, including FHE, organisations can ensure compliance with these regulations, safeguarding personal data throughout the training and inference phases of AI models.

Moreover, the Privasea AI Network is dedicated to protecting users' sensitive data from unauthorised access.

By encrypting sensitive data during AI processing and inferencing, the network fortifies defenses against data breaches and unauthorised intrusions.

Their privacy-preserving techniques in machine learning not only ensures regulatory compliance but also fosters trust among individuals, encouraging them to share their data with confidence.

So What is FHE?

Fully Homomorphic Encryption (FHE) is a transformative encryption methodology that permits intricate computations on encrypted data, ensuring that the decrypted results align perfectly with those that would have been achieved if the computations were executed on the unencrypted data.

To put it simply, in traditional data processing, calculations require data to be decrypted first, exposing sensitive information and increasing the risk of theft or misuse.

With FHE, encrypted data can be directly used for calculations, and the results remain encrypted until decryption is necessary.

This capability is crucial for industries that handle sensitive data, such as finance, healthcare, and government sectors.

Zama, a leading cryptography company, specialises in developing cutting-edge FHE solutions for blockchain and AI.

This partnership aims to advance FHE technology, focusing on practical applications in end-to-end encryption for machine learning.

By collaborating, Privasea and Zama aspire to become prominent projects in the Web3 landscape, driving innovation and security in the field.

Within the spectrum of FHE, encryption schemes are typically delineated into three distinct categories:

1) Somewhat Homomorphic Encryption (SHE): This scheme facilitates a predetermined number of addition and multiplication operations on the encrypted data, or ciphertext.

2) Fully Homomorphic Encryption (FHE): This robust scheme supports an unlimited number of addition and/or multiplication operations on the ciphertext, preserving its integrity throughout decryption.

3) Partial Homomorphic Encryption (Partial HE): This scheme enables either addition or multiplication operations on the ciphertext, but not both, offering a more specialised approach to encryption.

The intersection of machine learning (ML) with FHE is a burgeoning field that promises to revolutionise privacy-preserving computations.

Machine-learning process

Fully Homomorphic Encryption based Machine Learning (FHEML) represents a paradigm where machine learning algorithms are adapted to operate within the confines of FHE schemes.

This innovative approach ensures that computations on encrypted data yield results that are consistent with those of unencrypted data, thereby safeguarding the confidentiality of the processed information.

Computation on encrypted data

FHEML is at the forefront of enabling machine learning applications to handle encrypted data without compromising privacy or security.

It encompasses a suite of machine learning algorithms tailored to work seamlessly with FHE, unlocking a plethora of privacy-focused use cases.

These include confidential computing, encrypted training of models, and private inferences, among others.

The advancements in FHEML not only fortify data security but also broaden the horizons for machine learning applications in domains where privacy is paramount.

Privasea's Team Information

Below are some of the members of the team behind Privasea.

David Jiao (CEO)

David Jiao, the co-founder and CEO of Privasea, is a serial entrepreneur with a proven track record. He has successfully raised $20M for AI projects and $4M for blockchain initiatives.

Zhuan Cheng

Zhuan Cheng is the cryptographic expert at Privasea. He leads the research team, overseeing the design and development of cryptographic product architecture and blockchain solutions.

Jeffrey Duan

Jeffrey Duan, a professor and director of Applied Mathematics at the Illinois Institute of Technology, serves as the Chief Science Advisor for Privasea, guiding the design of cryptographic algorithms.

Ting Gao

Ting Gao, an Associate Professor at HUST and a former senior ML engineer at Twitter, is the Chief Data Scientist at Privasea. She leads the research team focusing on FHEML.

Lei Chen

Lei Chen is a senior researcher and system architect specialising in automotive product architecture and data solutions.

Noel Braganza

With a background in Digital Product Design from the MIT Design Lab, Noel Braganza leads Privasea's product innovation and design team, focusing on enhancing product experience and design strategy.

Sifan Lü

Sifan Lü is the tech coach for Privasea's engineering team. He has extensive experience in building pipeline systems and managing DevOps.

Martin Tang

A crypto and Web3 practitioner, Martin Tang is the co-founder and CMO of Privasea. Since 2022, he has been serving as an advisor to multiple Web3 projects and participate in the incubation of incubation rounds.

Privasea and Fhenix and Partner Up to Enhance Secure AI App Development

Towards the conclusion of last week, Fhenix, a layer 2 platform that harnesses FHE cryptography to empower Ethereum developers, embarked on a strategic partnership with Privasea.

This collaboration is designed to leverage the unique expertise and capabilities of both companies, with the aim of propelling the development of secure AI applications utilising FHE technology.

The synergy between Privasea and Fhenix is expected to yield significant advancements by converging blockchain and AI technologies.

The joint efforts will encompass the ongoing development of FHE libraries and the establishment of interoperability between the two entities.

Additionally, the partnership is set to initiate endeavours in hardware acceleration.

Both platforms will collaborate to enhance the TFHE-rs library developed by Zama, which serves as a foundational infrastructure component for both companies.

The teams from both firms are poised to integrate Privasea's applications into Fhenix's L2 infrastructure.

Furthermore, they will explore innovative mechanisms to incorporate additional homomorphic encryption schemes, such as CKKS/BGV/BFV, which support Single Instruction, Multiple Data (SIMD) parallel processing and data packing.

This integration will enhance support for high-precision and large-scale computing scenarios.

As a result of this partnership, a myriad of product opportunities will be unlocked.

The alliance will utilise FHE to introduce cutting-edge use cases, including AI, to the market.

Collaboration with Solana Launches ImHuman Application

Then earlier last week, Privasea integrated its technology into the Solana network, marking its debut as the inaugural Proof of Human application on the Solana blockchain.

Solana's exceptional performance and minimal latency render it an optimal blockchain platform to support Privasea's FHE technology and AI computing requirements.

Operating on the Solana blockchain, Privasea's ImHuman application is capable of verifying user identities on a broader scale, ensuring the network's security and reliability while safeguarding user privacy.

The ImHuman app employs users' biometric data to generate a distinctive digital identity.

Initially, users must scan their facial vectors through the app's front camera, a process that is conducted entirely on the user's device to prevent any leakage of sensitive data.

Subsequently, the data is encrypted and transformed into a non-fungible token (NFT) that represents the user's encrypted biometric vector.

This approach leverages the properties of FHE, which allows for intricate computations without decrypting the data, thereby maintaining data security and privacy.

During the user identity verification process, the ImHuman app re-scans the user's facial features and compares the freshly obtained data against the encrypted data housed on the blockchain.

This procedure also utilises FHE technology to ensure that the data remains encrypted throughout the verification process, effectively mitigating the risk of data exposure.

Furthermore, since each user's NFT is derived from their unique biometrics, it is inherently resistant to duplication or forgery, significantly raising the barrier against sybil attacks.

Through the ImHuman application, Privasea not only offers a potent tool to enhance the security of decentralised networks but also demonstrates the practicality of fully homomorphic encryption technology in real-world scenarios.

This biometric-based authentication method, coupled with FHE, delivers a secure and privacy-respecting solution for decentralised networks, positioning Privasea's ImHuman as the first application with the potential for widespread adoption in the FHE domain.

By incentivising user participation and continued engagement through airdrop rewards, ImHuman can further promote its extensive application.

This innovative solution introduces a novel strategy to counteract sybil attacks, underscoring Privasea's commitment to security and privacy in the digital realm.

Privasea Secured $5M in Funding to Pioneer DePIN with FHEML Technology

Privasea, an early beneficiary of Binance Labs' support, concluded a strategic private placement round just a few months ago.

This financing round, which attracted investors such as OKX Ventures, Nomura Group's Laser Digital, and the SoftBank-backed incubator Tanelabs, significantly bolsters Privasea's standing in the realms of artificial intelligence and data security.

The successful completion of this round is set to expedite Privasea's progress, propelling the company to deliver even more secure and efficient data processing solutions to a global user base.

Previously, Privasea also secured a $5 million pre-seed/seed funding round to spearhead the integration of FHEML into a distributed computing network.

This funding round was marked by the participation of visionary investors from the technology and blockchain sectors, including Binance Labs, Gate Labs, MH Ventures, K300, QB Ventures, Crypto Times, and esteemed industry angels like Zakaria (zak) Awes and Luke Sheng from Chainlink.

These strategic investments underscore the confidence that leading investors have in Privasea's innovative approach to data security and AI, positioning the company as a trailblazer in the field.

The list of some of the investors backing Privasea as seen on its website

Token Distribution

Privasea has a token supply of one billion PRVA.

Source: Cryptorank

Privasea's Roadmap for the Future

As the field of AI continues to evolve, the spotlight is increasingly turning to the quality of data.

Companies are intensifying their efforts to gain a competitive advantage in the data race, not only expanding their data sources but also ensuring compliance and respecting user privacy.

The imperative for these companies is to find a way to perform computations on encrypted data without compromising the security of user information.

Privasea presents the world with a solution that prioritises privacy—a network designed to safeguard sensitive data.

In essence, the advancement of AI technology is intrinsically linked to the quality of the data used for training.

Provided that data usage remains compliant and privacy is preserved, the demand for the Privasea Network has the potential to surge significantly.

Privasea's next phase