PREDICTIVE intelligence promote a more privacy-focused mindset going forwards . But there is still work to be done to make FHE faster and more efficient and broaden its use even more so .
At Zama , for example – where we ’ re using an extension of TFHE as the underlying FHE scheme – we ’ ve reduced the time needed for a key operation from 20 milliseconds to just three milliseconds on CPUs and expect to further improve this by 5 – 10x with new cryptography breakthroughs we ’ re working on .
Moving to GPUs and FPGAs should enhance performance by another 5 – 10x , giving us a potential 100-fold increase in efficiency compared to when we started .
While these improvements are already significant and benefit blockchain and AI applications , we ’ re
Only with a clearer understanding of HE can we be properly equipped to make informed decisions about which products and services align with our privacy expectations .
still working towards achieving an even greater 100-fold performance boost . Once hardware acceleration is addressed , FHE ’ s technical challenges are effectively addressed ; a situation we expect to see by 2026 , seeing FHE widely deployable across various platforms .
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