Courtesy: IBM Systems

Confidentiality is a cornerstone of financial systems. In traditional finance, privacy is fundamental, details like account balances, transaction histories, and sensitive personal data are securely guarded. However, with blockchain’s decentralization and transparency, every transaction is recorded on a public ledger, visible to anyone with an internet connection.

While transparency is a celebrated feature of blockchain, could it also be hindering its potential for certain on-chain applications? We think so.

Take, for instance, the use of blockchain as a payroll or bank account. Imagine your salary payments, savings, and investments were entirely visible to anyone who wished to look. Beyond personal privacy, this kind of transparency could disrupt businesses and institutions, making confidential financial operations impossible. For Web3 to support mainstream use cases like payroll, institutional finance, or private voting, we need confidentiality as much as we need transparency.

You may have heard of Zero-Knowledge Proofs (ZKPs), a popular cryptographic tool that enhances privacy by proving knowledge of information without revealing it. For example, with ZKPs, you could prove you’re over 18 without revealing your exact age, or confirm a transaction amount without disclosing the specific figures. ZKPs protect privacy in verification processes, but they have limitations when it comes to conducting operations directly on private data.

Limitations of Zero-Knowledge Proofs (ZKPs)

While ZKPs are powerful for verifying certain information without exposure, they fall short when more complex computations are required. For example, imagine a decentralized financial application that needs to perform calculations on encrypted user data, like calculating credit scores, managing salaries, or processing bids in a blind auction. ZKPs can confirm a transaction’s validity or compliance with specific rules, but they don’t support direct operations on the encrypted data itself.

Enter Fully Homomorphic Encryption (FHE), a cryptographic breakthrough that goes beyond verification to enable computation on encrypted data without the need for decryption.

Introducing Fully Homomorphic Encryption (FHE): Confidential Computing Without Compromise

FHE allows encrypted data to remain encrypted throughout computation, offering a powerful solution to the limitations of ZKPs. This means that operations, such as addition, multiplication, and other calculations, can be performed directly on encrypted data, and the result, when decrypted, will reflect the outcome as if it were computed on the original, unencrypted data.

To understand how transformative FHE is, consider these real-world applications:

1. Secure Financial Analytics: A bank could analyze encrypted loan applications and credit histories to calculate credit scores without ever seeing the sensitive financial data. When decrypted, only the final credit decision is revealed, protecting customer privacy throughout the entire evaluation process.
2. Private Healthcare Diagnostics: Medical AI models could process encrypted patient data (lab results, medical history, genetic information) to provide diagnostic recommendations while keeping all personal health information confidential. The healthcare provider only sees the final diagnostic suggestion after decryption.
3. Confidential Supply Chain Optimization: Multiple companies could collaborate on supply chain optimization by performing calculations on encrypted inventory levels, demand forecasts, and pricing data without exposing their proprietary information to competitors. Only the optimized supply chain recommendations are revealed when decrypted.

This ability to perform complex computations on encrypted data while preserving confidentiality unlocks powerful use cases in Web3 where privacy is paramount, especially in DeFi, healthcare dApps, and private enterprise blockchain solutions.

Theoretical Foundations of FHE

Courtesy Zama.AI