Fhenix announces its collaboration with Privasea, a pioneer in AI + DePIN computing with an emphasis on FHEML. Through this agreement, the development of secure AI applications employing FHE will be advanced by using the experience of both Privasea and Fhenix.
1/ Today we’re thrilled to announce a partnership with @Privasea_ai, a leader in FHEML-focused AI + DePIN computing.Together, we'll be working to advance the development of secure AI applications using Fully Homomorphic Encryption (#FHE). pic.twitter.com/OcJw59LdEO
— Fhenix (@FhenixIO) June 13, 2024
FHE’s Significance in the AI Era
Data privacy technologies are more important than ever since more and more of our data is being stored online. A strong and trustless privacy technology is required in this unstoppable digital revolution to protect our digital sovereignty. With the development of AI and Large Language Models (LLMs), which use enormous volumes of data for training, this need has only become more critical.
Because it enables direct processing on encrypted data, Fully Homomorphic Encryption (FHE) is the perfect encryption option for artificial intelligence. Because of this, LLMs can handle the enormous volumes of data required for blind operation without ever having to decode it. As a consequence, people’ interactions with AI are safer and more secure.
The partnership between Privasea and Fhenix will take use of each company’s special skills and knowledge to further research and development at the nexus of blockchain and artificial intelligence. The ongoing creation of FHE libraries, facilitating communication between the two groups, and hardware acceleration programs are some of these endeavors.
Software Development
Privasea and Fhenix will collaborate to expand Zama’s TFHE-rs library, which is an essential part of both projects’ architecture. Both groups will also strive to make it possible for Privasea apps to be integrated on top of Fhenix’s Layer 2 infrastructure.
Additionally, Fhenix and Privasea will investigate how to include other homomorphic encryption algorithms that support SIMD parallel processing and data packing and are based on CKKS/BGV/BFV. Better support for situations involving large-scale, high-precision computing will result from this, expanding the potential market for both parties’ products.
Hardware Acceleration
In addition to these software development endeavors, Fhenix and Privasea will collaborate on possible hardware acceleration paths. More specifically, exploring high-parallelism, high-performance hardware—such as GPUs and FPGAs—for hardware acceleration on the underlying NTT/FFT. It is anticipated that this hardware development would result in a major improvement in the efficiency of fully homomorphic encryption methods. The team is also exploring the possibility of programming custom ASIC chips to enhance Fhenix’s performance.
Conclusion
A major advancement in the integration of Fully Homomorphic Encryption into AI and blockchain applications is the Fhenix-Privasea partnership. The team can’t wait to collaborate with the Privasea team and use its complementary skills to propel FHE into the AI era.
Confidential smart contracts will be created using FHE in the next generation of blockchain apps. Numerous new use cases, including AI, are made possible by this innovative cryptographic technique.
Fhenix’s documentation is an excellent place to start if you’re an application developer looking for additional information. The team would be happy to address any inquiries you may have in its Discord channel about Fhenix or FHE.