Messari Quick Overview of Fully Homomorphic Encryption Projects
Messari's Concise Overview of Fully Homomorphic Encryption InitiativesAuthor: Sami Kassab, Messari Researcher; Translator: LianGuai0xjs
Fully Homomorphic Encryption (FHE), also known as the Holy Grail of computation and black magic, has its reasons. (See LianGuai’s previous article: Why fully homomorphic encryption is the next big crypto story)
FHE allows for computing on encrypted data without decryption, which essentially means that sensitive data can be processed without being leaked.
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The transparency of blockchain poses challenges for businesses adopting it, as most enterprises are unwilling to reveal various aspects of their operations on public chains. Using FHE to encrypt and compute on-chain data could be a solution to this problem.
Won’t ZKP solve the privacy problem of cryptocurrencies? Maybe not.
ZKP is not a good privacy solution for two reasons:
1. For most applications, a third party with more powerful machines is needed to generate proofs, exposing user data. Users must trust these entities and their data.
2. ZKP is not suitable for applications that require both global and private states simultaneously. Using ZKP to protect privacy is applicable to simple single-client use cases like Zcash, but not for applications like Uniswap that require global state.
Coming back to FHE, the development of FHE has recently surged in response to strict data regulations such as GDPR by tech giants (e.g., Meta, Google, Amazon) and increasing funding from government institutions amidst growing cybersecurity threats.
The collective efforts of leaders in the Web 2 industry have given rise to a plethora of SDKs and open-source libraries specifically for FHE. Coupled with broader advancements in the field, this paves the way for FHE to integrate into the blockchain space.
The most anticipated breakthrough is L1 support for FHE, which will provide end-to-end encryption for public chains. This means all on-chain data will be private. Even validators will gain knowledge of transaction data, eliminating MEV. Fhenix is one such project.
Using FHE enables various other use cases, including private payments and smart contracts, trustless games like poker, private DAO voting, private machine learning, private databases, and the private data economy.
FHE is not a silver bullet. Many implementations require collaboration with ZK and multiparty computation technologies to provide comprehensive privacy solutions. It also faces many barriers to widespread adoption, including the need for significant computational resources and “noise”.
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