This post is a high-level summary of our recent webinar ‘Building a better web on encrypted blockchain’. You can catch up on the replay here →

The imperative for privacy

Blockchain is transparent by desigg. While powerful, this transparency is a significant limiting factor in many applications, from Web3 and DeFi to TradFi. There is an increasing demand from institutions and regulators for greater privacy and confidentiality in blockchain processing.

Solving this challenge is key to unlocking the full potential of blockchain, onboarding the next millions of users and billions in value.

The scope of this required confidentiality goes beyond simple tplem transactions. For account-based blockchains like Ethereum, privacy must extend to protecting the entire persistent state of the network, and even the functions being executed within smart contracts.

Understanding the tools: ZKPs and FHE 

ZKPS: The power of verification

ZKPs have seen significant traction in recent years; they enable a ‘prover’ to convince a ‘verifier’ about a property of a state or transaction, without exposing any of the underlying private data. A common analogy is proving you are over 21 without showing your driver’s license, only a simple cryptographic proof that can be validated on-chain.

This technology is quite mature, supported by extensive tooling, and is very efficient for these types of verification tasks.

However, ZKPs have limitations. They do not, in most cases, allow for general computation on encrypted data that might be stored on-chain. While state transitions are possible in some limited circumstances, it is difficult to use ZKPs alone for one party to directly alter another party’s private state.

“The biggest barrier to widespread enterprise adoption of blockchain is privacy” – Jeremy Allaire, CEO of Circle (issuer of USDC stablecoin) 

FHE: Enabling absolute privacy 

ToFHE offers a fundamentally different capability: the ability to perform direct computation and execute elementary operations on encrypted data. This unlocks more complex applications that ZKPs on their own cannot support, such as advanced DeFi operations between private user positions, online auctions with encrypted bids, and multi-party data analytics. 

With a powerful FHE accelerator, you could process encrypted token transfers at the scale required for mainstream application, or train a machine learning model directly on encrypted data. With ZKP, you could only prove that the model was trained using the correct functions. This ability for direct computation allows FHE to enable true programmable privacy, including multi-party smart contracts. 

A complementary future: ZKPs and FHE in synergy

The future of on-chain privacy is not a choice between one technology or the other; it relies on their complementarity. FHE can be used to perform a private computation, while a ZKP can then be used to prove that the computation was executed correctly without revealing any of the encrypted data. 

This synergy is extremely important for solving major security challenges, such as designing trustless bridges for cross-chain transfers. Malfunctions in these bridges are currently a source of billions of dollars in stolen crypto assets per year, and a combination of these technologies is crucial to address this vulnerability. 

To realise this vision, the ecosystem must address several key challenges. One of the biggest, is that FHE is a newer technology limited by the processing capability of conventional processors. Further development of tooling is also required to make FHE simpler for everyday programmers to use.  

Finally, because FHE ciphertexts can be very large, they are not practical to store directly on-chain, necessitating data availability layers where only a small fingerprint of the data resides on the blockchain. 

The most critical challenge is performance – the bulk of FHE processing relates to transform functions and is incredibly computationally intensive. Hardware acceleration is essential to provide the speed and efficiency required to make these systems commercially viable and usable.  

This is the focus for us at Optalysys, where we are developing photonic technology is designed to accelerate these fundamental functions far beyond what can be achieved with digital processing. 

By solving the performance bottleneck, hardware acceleration will unlock the full potential of FHE, allowing it to become a seamless background layer within the fabric of a more secure, private, and capable Web3. 

At Optalysys we’re developing the future of encrypted blockchain through pioneering the use of optical computing to accelerate Fully Homomorphic Encryption. Find out more about LightLocker Node and how we can accelerate your confidential blockchain use case → 

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