Back to the list

Making Privacy Solutions EVM-Compatible Is Key to Integrating Them With Blockchains and Dapps — Guy Itzhaki

source-logo  news.bitcoin.com 21 December 2023 07:50, UTC

While proponents of fully homomorphic encryption (FHE) have sometimes touted it as a better privacy solution than zero-knowledge (ZK) proofs, Guy Itzhaki, the founder and CEO of Fhenix, said both are cryptographic-based technologies which, when combined, can form a robust and efficient encryption layer. To support this viewpoint, Itzhaki pointed to a research study whose findings suggest that “combining ZKPs with FHE could achieve fully generalizable, confidential decentralized finance (defi).”

The Blockchain and AI Converging

Despite their great promise, privacy solutions have yet to become an important part of blockchains and decentralized apps (dapps). In his written answers sent to Bitcoin.com News, the Fhenix CEO said one of the reasons for this may be the perceived burden they bring to developers and users. To overcome such problems, Itzhaki proposed making these solutions EVM-compatible and also bringing FHE encryption capabilities to the programming language Solidity.

Meanwhile, when asked how developers and users can protect their privacy in a world where blockchain and artificial intelligence (AI) are converging, the founder of Fhenix — an FHE-powered Layer 2 — said that the first step would be to raise awareness about the presence of emerging risks or challenges. Taking this step will force developers to design applications that address these challenges.

For users, Itzhaki said the best way to protect themselves is to “educate themselves about safe usage and utilize tools that support personal data protection.” Elsewhere, in his answers sent via Telegram, Itzhaki also touched on why the much-vaunted Web3 mass adoption has not come.

Below are Guy Itzhaki‘s answers to all the questions sent to him.

Bitcoin.com News (BCN): Quite often, the lack of a refined user experience is seen as the biggest roadblock to Web3 mass adoption. However, some see privacy concerns as another major obstacle, especially for institutional adoption. In your opinion, what do you see as the biggest obstacles the Web3 ecosystem needs to collectively overcome to become commonplace?

Guy Itzhaki (GI): First of all, a lack of a sense of security while interacting with blockchain-based applications. Many people are deterred from using it because it “feels” less secure than traditional applications that offer “built-in” security, even at the cost of centralization.

The second challenge is the general bad user experience that the space commits you to. For example, the sense of security (or functionality) is damaged greatly when users lose funds due to small operating mistakes that might happen to anyone. The complicated nature of operating most decentralized applications is a huge obstacle to mass adoption.

Another issue is regulations. Blockchain adoption is hindered by the negative sentiment of regulators and traditional markets, mainly due to associations with criminal activity- we need to find a way to allow users to keep their data private (on public blockchains) while also allowing them to be compliant with the law.

FHE technology holds a lot of potential for coping with these challenges (through encrypted computation function). By introducing native encryption to the blockchain, we can facilitate a better sense of security (for example by encrypting the user’s assets balance), support applications like account abstraction that significantly reduce the user’s complexity when interacting with the blockchain and enable decentralized identity management that is needed for compliance.

BCN: Depending on the products and use cases, the blockchain ecosystem has a wide range of privacy needs. Do you see FHE replacing zero-knowledge ZK proofs and trusted execution environments (TEEs) or can these innovative technologies co-exist?

GI: That’s a great question as there’s a serious discussion regarding the efficacy of any single privacy-preserving technology to solve all data encryption needs and scenarios- Due to extreme differences between competing encryption technologies (cost, complexity, UX)..

It is important to understand that while both FHE and ZKP are cryptographic-based technologies, they are very different. ZKP is used for the verification of data, while FHE is used for the computation of encrypted data.

Personally, I believe that there isn’t a ‘one-stop-shop’ solution, and probably we’ll see a combination of FHE, ZKP and MPC technologies that form a robust, yet efficient encryption layer, based on specific use case requirements. For example, recent research has shown that combining ZKPs with Fully Homomorphic Encryption (FHE) could achieve fully generalizable, confidential DeFi: ZKPs can prove the integrity of user inputs and computation, FHE can process arbitrary computation on encrypted data, and MPC will be used to separate the keys used.

BCN: Can you tell us about your project Fhenix and the fully homomorphic encrypted virtual machine (fhEVM) as well as how it blends into the existing chains and platforms?

GI: Fhenix is the first Fully Homomorphic Encryption (FHE) powered L2 to bring computation over encrypted data to Ethereum. Our focus is to introduce FHE technology to the blockchain ecosystem and tailor its performance to Web3 needs. Our first development achievement is the FHE Rollup, which unlocks the potential for sensitive and private data to be processed securely on Ethereum and other EVM networks.

Such advancement means that users (and institutions) can conduct encrypted on-chain transactions, and it opens the door for additional applications like confidential trustless gaming, private voting, sealed bid auctions and more.

Fhenix utilizes Zama’s fhEVM, a set of extensions for the Ethereum Virtual Machine (EVM) that enables developers to seamlessly integrate FHE into their workflows and create encrypted smart contracts without any cryptographic expertise, while still writing in Solidity.

We believe that by bringing devs the best tools for employing FHE on top of existing protocols will pave the way for the formation of a new encryption standard in Web3.

BCN: Whether it’s FHE, ZK proof or something else, the privacy solutions themselves have an uphill task to become an integral part of blockchains and decentralized apps (dapps). What factors or strategies would make it easier for builders to integrate privacy solutions into the existing chains and platforms?

GI: I come from a very practical background, and that is why when we just started designing Fhenix, it was clear to us that we needed to make FHE as easy as possible for developers and users. As such our first decision was to make sure we are EVM compatible and bring the FHE encryption capabilities in Solidity in order to reduce the burden on developers, and not require them to learn a new, specific language for coding. That also means that developers do not need to hold any cryptographic expertise or FHE knowledge for developing dapps.

Lastly, we’re solving for developer experience in developing encryption-first, applications. That means that we focus on creating the best stack for developers, to ease the development process as much as possible.

BCN: With FHE, one can input data on-chain and encrypt it while being able to use it as if it’s non-encrypted. The data is said to remain encrypted and private during transactions and smart contract implementations. Some believe that this level of on-chain privacy could go beyond solving privacy issues and unlock use cases that weren’t possible before. Could you illustrate through examples some of these potential use cases, if any?

GI: In terms of relevant use cases, every application that requires data encryption can benefit from utilizing FHE in some form or another. The most interesting use cases are those that benefit greatly from performing computations on encrypted data, like:

  • Decentralized identity
  • Confidential Payments
  • Trustless (Decentralized) gaming
  • Confidential defi

One great example is Casino gaming. Imagine a scenario where the dealer distributes cards without knowing their values—a glimpse into the potential of fully private on-chain encryption. This is just the beginning. FHE’s ability to incorporate data privacy and trust into the blockchain is essential for both game makers and players, and fundamental to future gaming innovations and use cases.

One promising avenue for achieving this is through Fhenix’s FHE Rollups, which empower developers to create custom app chains with FHE seamlessly integrated, all while using familiar Ethereum Virtual Machine (EVM) languages.

In the context of gaming, FHE Rollups offer the ability to build gaming ecosystems with FHE technology at their core. For instance, one roll-up could be dedicated entirely to casino games, ensuring the complete privacy and security of these games. Meanwhile, another rollup, fully interoperable with the first, could focus on large-scale player-versus-player (PvP) games.

BCN: Artificial intelligence (AI) and blockchain, two of some of the hottest technologies right now, appear to be converging. Now some people believe AI could have both positive and negative impacts on Web3 user privacy and safety. Focusing on the negative effect, what precautionary measures should developers and users take to safeguard on-chain privacy?

GI: The first thing would be raising awareness of the growing challenges in the internet, and in Web3 space in particular, which should commit builders to consider these risks when designing their applications. Users, on the other hand, need to educate themselves about safe usage and utilize tools that support personal data protection.

In terms of technological precautionary measures- one of the use cases I am personally interested in is how we, the users, can tell the difference between AI-generative content and human-made content. Attesting to the origin of the content is a key feature of blockchains, and I am confident we will see apps that help track data origin in the future.

Specifically, for FHE, we are exploring ways to help create better AI modules by allowing users to share their data for AI training, without the risk of losing their privacy.

What are your thoughts about this interview? Let us know what you think in the comments section below.