We’re pleased to announce a partnership with Privasea, a leader in FHEML-focused AI + DePIN computing. The partnership will utilize both Privasea and Fhenix’s expertise to advance development of secure AI applications using FHE. 

The Importance of FHE in the AI Age

As an ever-increasing amount of our data moves online, the need for data privacy technologies has never been greater. In this inexorable march towards digitization, a robust and trustless privacy technology is needed to safeguard our digital sovereignty. This necessity has only grown more obvious with the advent of AI and Large Language Models (LLMs), which utilize massive amounts of data for training. 

Fully Homomorphic Encryption (FHE) is an ideal encryption solution for AI because it allows for direct computation on encrypted data. This enables LLMs to process the vast amounts of data needed to operate blindly, without ever needing to decrypt it. This results in a safer, more secure relationship between users and AI. 

The collaboration between Fhenix and Privasea will leverage the unique talents and expertise of both parties to progress development at the intersection of AI and blockchain. These efforts include the continued development of FHE libraries, providing interoperability between the two projects, and hardware acceleration initiatives.

Software Development

Both Fhenix and Privasea will work together to extend Zama’s TFHE-rs library, a core infrastructure component of both projects. Additionally, both teams will work to enable the integration of Privasea applications on top of Fhenix’s Layer 2 infrastructure. 

Fhenix and Privasea will also explore ways to integrate additional homomorphic encryption schemes based on CKKS/BGV/BFV, which allow data packing and SIMD parallel processing. This will provide better support for large-scale, high-precision computing scenarios, widening the product possibilities for both parties.

Hardware Acceleration 

Alongside these software development initiatives, Fhenix and Privasea will also work in tandem on potential avenues for hardware acceleration. Specifically, we’re looking at hardware acceleration on the underlying NTT/FFT using high-parallelism, high-performance hardware such as GPUs and FPGAs. This hardware advancement is expected to provide a significant breakthrough in the efficiency of fully homomorphic encryption algorithms. We’re also taking a look at programming custom ASIC chips to improve Fhenix’s performance.

Conclusion

The Fhenix-Privasea collaboration represents a significant step forward in the integration of Fully Homomorphic Encryption into AI and blockchain applications. We’re excited to work with the Privasea team to leverage both of our unique talents to usher FHE into the AI Age.

The next generation of blockchain applications will utilize FHE to create confidential smart contracts. This novel cryptographic primitive opens up a slew of new use cases, including AI.

If you’re an application builder who wants to learn more, our docs are a good place to start.

If you have any questions about Fhenix or FHE, we’d love to answer them in our Discord.

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