In September this year, an interesting development occurred in the field of cryptography—an open-source FHE library's performance was directly boosted by 80%. While this may seem like a technical detail, it actually has a significant impact.

So, how was this breakthrough achieved? There are a few key points: the speed of encrypted computations was optimized from being 80 times slower to only 15 times slower, which is a substantial improvement; more importantly, real-time privacy computation was successfully run on consumer-grade GPUs for the first time—previously, this was only possible on servers; it also supports elliptic curve ciphertext compression, significantly reducing data size.

Privacy-oriented public chains like Mina and Aztec have announced plans to integrate this technology, and cryptography professors from Princeton have also spoken out—this technology makes "achieving true privacy protection on blockchain without relying on trusted execution environments" a reality. What was once just an idea is now practically implementable.

In terms of applications, scenarios such as private voting, medical data analysis, and confidential financial transactions can all be expanded. In simple terms, this breakthrough is like installing a new engine for privacy computing.