fully homomorphic encryption

Fully homomorphic encryption, also known as the Holy Grail of cryptography, allows for computation to be performed on encrypted data, without the need for prior decryption. Its blockchain applications would enable programmable, institutional-grade, compliant privacy. With the addition of fhEVM libraries, solidity developers don’t have to worry about the complex cryptography and only decide what layers of the UX should be private.

We were joined by Guy Itzhaki & Guy Zyskind, to discuss fully homomorphic encryption and how Fhenix plans to leverage it to build an end-to-end encrypted Ethereum L2 with compliant privacy.

Topics covered in this episode:

• Guy(s)’ backgrounds and why they chose FHE
• The differences between TEE, MPC, ZK and FHE
• Threshold decryption
• The challenges with FHE. Hardware acceleration. Zama’s fhEVM
• Fhenix architecture
• fhEVM
• Use cases for FHE & composability
• Compliant privacy
• Fhenix roadmap

Episode links:

• Guy Zyskind on Twitter
• Guy Itzhaki on Twitter
• Fhenix on Twitter
• Encryption Day @ ETH Denver (Feb. 28, 2024) registration

Sponsors:

• Gnosis: Gnosis builds decentralized infrastructure for the Ethereum ecosystem, since 2015. This year marks the launch of Gnosis Pay— the world's first Decentralized Payment Network. Get started today at - gnosis.io
• Chorus1: Chorus1 is one of the largest node operators worldwide, supporting more than 100,000 delegators, across 45 networks. The recently launched OPUS allows staking up to 8,000 ETH in a single transaction. Enjoy the highest yields and institutional grade security at - chorus.one

This episode is hosted by Felix Lutsch. Show notes and listening options: epicenter.tv/530

Homo- (Greek prefix meaning ‘same’); -morphic (Greek suffix meaning ‘having a specific shape/form’)
Intuitively, one could deduct that homomorphic encryption indicates that the initial data and the encrypted result (cipher) could share the same form. Based on this property, it can be inferred that computation can be performed on the encrypted data, without prior decryption. By decrypting the result, you get the same output as the computation performed on the unencrypted data. While homomorphic encryption can be either additive or multiplicative, fully homomorphic encryption supports both types of operations. Unlike ZKPs, which are proofs of computational integrity, fully homomorphic encryption allows for encrypted data computation, without revealing additional information about the original data. This could provide the missing link for ensuring private transactions on blockchains’ public ledgers.

We were joined by Rand Hindi, CEO of Zama, to discuss fully homomorphic encryption solutions, how they differ from ZKPs & MPC, and how they can be leveraged to ensure compliant programmable privacy.

Topics covered in this episode:

• Rand’s background and his interest in privacy
• Meeting Pascal and founding Zama
• Fully homomorphic encryption (FHE)
• Zero knowledge proofs vs. Multi-party computation vs. Fully homomorphic encryption
• Taking fully homomorphic encryption 'mainstream'
• Zama’s products
• fhEVM
• How multi-party computation would secure fhEVM
• Multi-key homomorphic encryption & functional encryption
• Deploying an FHE rollup
• FHE use cases
• Privacy
• Zama’s business model

Episode links:

• Rand Hindi on Twitter
• Zama on Twitter
• Fhenix on Twitter

Sponsors:

• dYdX Foundation: The recently launched dYdX chain features new governance and token economics, that empower stakers and promote validator decentralisation. Bridge your DYDX tokens and contribute to the evolution of dYdX chain, fully permissionless and community driven. - https://bit.ly/47kqG59

This episode is hosted by Friederike Ernst. Show notes and listening options: epicenter.tv/523