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 cryptography-based technologies that, when used combined, form a robust and efficient encryption layer. To support this position, Itzhaki pointed to a study whose findings suggest that “combining ZKPs with FHE could achieve fully generalizable, confidential decentralized finance (defi).”
The convergence of Blockchain and AI
Despite their great promise, privacy solutions have yet to become an important part of blockchains and decentralized apps (dapps). In his written responses sent to Bitcoin.com News, the Phoenix The CEO said one reason 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 coding capabilities to the Solidity programming language.
Meanwhile, the founder of Fhenix, an FHE-powered Layer 2, when asked how developers and users can protect their privacy in a world where blockchain and artificial intelligence (AI) converge, said 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 practices and use tools that support the protection of personal data.” Elsewhere, in his replies sent via Telegram, Itzhaki also addressed why the much-vaunted mass adoption of Web3 is not happening.
Below you will find Guy Itzhaki’s answers to all questions sent to him.
Bitcoin.com News (BCN): Very often, the lack of a refined user experience is seen as the biggest obstacle to mass adoption of Web3. However, some see privacy issues as another major obstacle, especially to institutional adoption. What do you see as the biggest obstacles the Web3 ecosystem must collectively overcome to become mainstream?
Guy Itzjaki (GI): First of all, a lack of sense of security while interacting with blockchain-based applications. Many people are put off 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 overall poor user experience that the space forces you to. For example, the sense of security (or functionality) is seriously damaged when users lose money due to minor operating errors that can happen to anyone. The complicated nature of using most decentralized applications is a major obstacle to mass adoption.
Another problem is regulations. Blockchain adoption has been hampered by negative sentiment from regulators and traditional markets, mainly due to associations with criminal activities. We need to find a way to allow users to keep their data private (on public blockchains) while at the same time being compliant with the law.
FHE technology offers many possibilities to overcome these challenges (via a coded calculation function). By introducing native encryption into the blockchain, we can facilitate a better sense of security (e.g. by encrypting the user’s asset balance), support applications such as account abstraction that significantly reduce the user’s complexity when interacting with the blockchain, and enable decentralized identity management. necessary for compliance.
BCN: Depending on the products and use cases, the blockchain ecosystem has a wide range of privacy needs. Do you see FHE zero-knowledge replacing ZK-proofs and Trusted Execution Environments (TEEs) or can these innovative technologies coexist?
GI: That’s a great question, as there is a serious debate about the effectiveness of a 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 although both FHE and ZKP are cryptography-based technologies, they are very different. ZKP is used for the verification of data, while FHE is used for the calculation of encrypted data.
Personally, I believe there is no ‘one-stop-shop’ solution, and we will likely see a combination of FHE, ZKP and MPC technologies forming a robust, yet efficient encryption layer based on specific usage requirements. . For example, recent research has shown that combining ZKPs with fully homomorphic encryption (FHE) could yield a fully generalizable, confidential DeFi: ZKPs can prove the integrity of user input and calculations, FHE can handle arbitrary calculations 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 coded virtual machine (fhEVM) and how it fits into the existing chains and platforms?
GI: Fhenix is the first fully Homomorphic Encryption (FHE) powered L2 that brings computation over encrypted data to Ethereum. Our focus is on introducing FHE technology to the blockchain ecosystem and tailoring its performance to Web3’s needs. Our first development achievement is the FHE Rollup, which unlocks the potential for sensitive and private data to be securely processed on Ethereum and other EVM networks.
Such advancement means users (and institutions) can conduct encrypted on-chain transactions, and it opens the door for additional applications such as confidential, trustworthy gaming, private voting, sealed bid auctions, and more.
Fhenix uses Zama’s fhEVM, a set of extensions for the Ethereum Virtual Machine (EVM) that allows 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 providing developers with the best tools for using FHE on top of existing protocols will pave the way for the formation of a new encryption standard in Web3.
BCN: Whether FHE, ZK-proof or something else, the privacy solutions themselves have a tough job of becoming an integral part of blockchains and decentralized apps (dapps). What factors or strategies would make it easier for builders to integrate privacy solutions into existing chains and platforms?
GI: I have a very practical background and so it was clear to us when we first started designing Fhenix that we needed to make FHE as easy as possible for developers and users. Therefore, our first decision was to ensure we are EVM compatible and bring the FHE coding capabilities into Solidity to reduce the burden on developers and not require them to learn a new, specific language for coding. This also means that developers do not need to have cryptographic expertise or FHE knowledge to develop dapps.
Finally, we look for developer experience in developing applications where encryption is a priority. This means that we focus on creating the best stack for developers, to simplify the development process as much as possible.
BCN: FHE allows data to be entered on-chain and encrypted, while being able to use it as if it were unencrypted. 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 concerns and unlock use cases that were not previously possible. Can you provide examples to illustrate some of these possible use cases, if any?
GI: In terms of relevant use cases, any application that requires data encryption can benefit from using FHE in some form. The most interesting use cases are those that benefit greatly from performing calculations on encrypted data, such as:
- Decentralized identity
- Confidential payments
- Reliable (decentralized) gaming
- Confidential defi
A good example is casino gaming. Imagine a scenario where the dealer deals 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 integrate data privacy and trust into the blockchain is essential for game makers and players alike, and fundamental to future gaming innovations and use cases.
A promising way to achieve this is through Fhenix’s FHE Rollups, which allow developers to create custom app chains that seamlessly integrate FHE while using the well-known Ethereum Virtual Machine (EVM) languages.
In the context of gaming, FHE Rollups provide the opportunity to build gaming ecosystems with FHE technology at their core. For example, one roll-up could be completely dedicated to casino games, ensuring the complete privacy and security of these games. Meanwhile, another package, fully interoperable with the first, could focus on large-scale player-versus-player (PvP) games.
BCN: Artificial intelligence (AI) and blockchain, two of the hottest technologies of the moment, seem to be growing closer together. Some people now believe that AI could have both positive and negative impacts on the privacy and security of Web3 users. What precautions should developers and users take to protect privacy in the chain, with an emphasis on the negative effect?
GI: The first thing we need to do is raise awareness of the growing challenges on the Internet, and in the Web3 space in particular, which should prompt builders to consider these risks when designing their applications. Instead, users should inform themselves about safe use and use tools that support the protection of personal data.
In terms of technology precautions, one of the use cases I’m personally interested in is how we, the users, can tell the difference between AI generative content and human-generated content. Confirming the origin of content is a key feature of blockchains, and I am confident that in the future we will see apps that help trace the provenance of data.
Specifically for FHE, we’re exploring ways to help create better AI modules by letting users share their data for AI training, without the risk of losing their privacy.
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