Privasea and Zama reach cooperation to jointly explore AI, data security, and ML fields

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Recently, the Privasea ecosystem has continued to make substantial ecological progress. After obtaining a new round of strategic private equity financing, it has reached a strategic partnership with Zama, and the two parties will be bound together for a long time. This not only shows Zamas recognition of Privaseas technical solutions, long-term development prospects and narrative direction, but also marks an important progress in the application of privacy protection in the field of machine learning.

Zama is a technology company dedicated to promoting the application of fully homomorphic encryption (FHE) in the fields of blockchain and artificial intelligence. In the past four years, it has continuously transformed FHE from abstract mathematical theory into practical code, greatly improving developers ability to apply FHE technology. Recently, Zama successfully raised $73 million in its Series A financing, which further confirmed the markets confidence in Zamas technical strength and development potential.

Currently, Zama has developed developer-friendly FHE libraries and solutions, and continues to optimize performance. Its fhEVM has achieved remarkable results in privacy smart contracts for blockchain applications, and has been integrated with multiple projects including Fhenix, Shiba Inu and Inco. These collaborations mark progress in the practicality of privacy protection in blockchain.

Artificial intelligence and machine learning are potential areas where Zama hopes to promote the widespread adoption of FHE technology. For example, its Concrete ML tool serves these cutting-edge technology fields. In order to make further substantial progress in this direction, Zama has recently reached a strategic partnership with Privasea and formed in-depth technical integration with each other. Based on this, Zama will provide long-term technical support for Privaseas FHEML solution, and Privasea will also become an important piece of the puzzle for Zama to apply its FHE solution to the fields of AI and ML.

Through this cooperation, Privasea and Zama will play an indispensable role in each others ecosystem, and the two ecosystems will be bound together for a long time. At the same time, the establishment of this partnership also indicates that the application of privacy protection technology in the field of machine learning is about to usher in a new breakthrough.

The Narrative of Privasea Ecosystem

Privasea is a privacy computing platform driven by AI+DePIN and based on FHE technology. It is committed to providing a secure and sustainable AI and machine learning computing environment. We have seen that in the field of AI, data security and privacy protection have always been a long-term and complex challenge, and Privaseas FHE solution has a high degree of data confidentiality and compliance, and can comply with laws and regulations such as the EU General Data Protection Regulation (GDPR).

The core of the Privasea network is a powerful FHE pipeline, which is based on TFHE-RS and Concrete-ML and is specially customized for Privasea needs. This component provides a solid fortress for data security, so that users data is encrypted and protected throughout the entire computing process, and will not be leaked even during collaborative computing. So we see that Zama is an important source of Privasea FHE, and its support for the Privasea network is long-term and continuous.

Privasea not only provides developers with tools and functions to access the Privasea AI network capabilities through its API, supporting them to seamlessly integrate AI capabilities into their applications while ensuring data security and privacy. At the same time, Privasea has also launched a computing-centric DePIN network, Privanetix, which gathers decentralized computing resources to enable secure and efficient processing of encrypted data. Each node in the network is equipped with an applicable FHEML pipeline, allowing decentralized nodes to efficiently perform machine learning calculations without exposing sensitive data.

The Privanetix network is driven by Privaseas smart contract suite, which ensures that computing nodes in the network can be accurately tracked and rewarded. Smart contracts provide incentives for network participants while maintaining transparency and fairness, and are the economic foundation for the sustainable operation of the entire network.

Another advantage of Privasea is that it supports users without cryptography or programming background to easily access and utilize the capabilities of this network. This not only greatly reduces the threshold for using advanced FHE AI computing, allowing more users to safely enjoy the convenience brought by AI, but also seamlessly expands this set of privacy AI computing capabilities to various fields. At the same time, while protecting user data, the Privasea network supports compliance audits and meets various national laws and regulations, including anti-money laundering laws. This off-chain computing solution not only ensures data security, but also ensures that the networks behavior can be reviewed when necessary, providing users with a safe and reliable computing environment.

Based on this, Privasea can be highly integrated with many scenarios with verification, computing and analysis requirements for data protection. Potential scenarios include biometrics, medical care, finance, secure cloud data computing, anonymous voting systems, etc.

We see that Privasea is promoting the large-scale adoption of FHE solutions, while providing impetus for AI technology to be deeply integrated with various scenarios and widely adopted while ensuring data security and complying with data regulations. This ecosystem is also expected to become a new value carrier for the trillion-dollar application market.

Privasea and Zamas two-way journey

At present, the cooperation between Privasea and Zama has made substantial progress, and the core of the cooperation between the two lies in algorithm integration. Privasea currently not only supports the mainstream TFHE solution, but also integrates Zamas advanced TFHE-rs library into its own network to improve the privacy and security of AI operations. At the same time, Privasea will work closely with Zamas technical team to ensure that the TFHE solution can be seamlessly integrated into Privaseas infrastructure. Through stress testing and security audits on the test network, both parties will jointly ensure the stability and security of technical integration.

In addition to system integration, Privasea and Zama will jointly explore and develop emerging features such as ZAMAs global key model to lay the foundation for future deployment. At the same time, Privasea will also develop a series of privacy-preserving AI applications based on the ZAMA-ConcreteML platform. These applications cover key areas such as biometrics, medical image recognition, and financial data analysis, and plan to test the applications on its own network. Through these specific application cases, Privasea and Zama can convert theory into actual user value, and also verify the effectiveness of encryption technology in real environments.

In addition, Privasea and Zama also plan to establish a knowledge sharing platform through regular technical seminars and workshops to promote the exchange of technology, ideas and best practices. In addition to a series of technical cooperation, the two also plan to conduct in-depth cooperation at the market level, such as market expansion strategies, joint product promotion, etc.

So overall, Privasea will enhance the privacy and security of AI operations by integrating Zamas TFHE-rs library into its network, and this new integration will accompany the development of the Privasea ecosystem. As an ecosystem that has already seen the application of FHE technology to AI, ML and other fields, Privasea is expected to provide long-term support for Zamas AI layout and provide important support for the computing resources required for the FHE solution, which is something that Zamas other partners cannot provide. On this basis, Privasea is expected to become an important piece of the puzzle for Zama to apply its FHE solution to the fields of AI and ML.

In fact, Privasea and Zama have the same ecological vision. On the one hand, both are promoting the adoption of FHE technology in different fields. At the same time, both parties are supporters of AI, ML and other fields. This cooperation is also seen as a two-way rush.

Zama鈥檚 New Web3 Profile

The Zama ecosystem has the vision of building an end-to-end encrypted network HTTPZ (Z stands for Zero Trust). To this end, on the one hand, it provides an open source FHE tool ecosystem for Web2 and Web3 applications. By building multiple open source products, it enables developers to more easily use FHE for various use cases in fields such as blockchain and artificial intelligence.

On the other hand, it is building a new Web3 framework to better realize its vision.

We can see that Zama has currently established cooperation with three different Web3 facilities with FHE as the core technology, including Inco, Fhenix and Privasea, to further expand its FHE technical solutions. Among them, Inco represents the Layer 1 direction of the Zama framework, Fhenix represents the Layer 2 direction, and Privasea is the Depin AI facility direction.

Based on Zamas open source framework, FHE technology will be expected to expand into more fields and build a series of innovative emerging products that can solve practical problems, thereby enhancing the privacy of the Web3 world and establishing new standards in the field of online data privacy with its ecosystem partners.

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