The cryptocurrency industry has always walked a fine line between transparency and privacy. Public ledgers like Bitcoin ensure transparency, but the lack of privacy poses challenges for individuals and enterprises alike. It was therefore a welcome signal from U.S. regulators when U.S. courts threw out the OFAC case against TornadoCash, a mainstay privacy “mixer” protocol. Privacy is back in vogue in 2025, so what’s next? 

Enter Fully Homomorphic Encryption (FHE)—a cutting-edge cryptographic solution that can reshape how privacy and data security are handled in crypto.

FHE has the potential to address some of crypto’s most pressing privacy and scalability challenges. FHE can run computations on encrypted data, and this unlocks use-cases previously deemed impractical – such as private smart contracts, confidential DeFi transactions, and secure voting in DAOs.

Let’s dive into how FHE works, its advantages over existing solutions, and how Web3 projects like Zama and Fhenix are applying this revolutionary technology to the crypto space.

What is Fully Homomorphic Encryption (FHE)?

In cryptocurrency, privacy and security are paramount, but they often come at a cost to usability or performance. FHE offers a novel way to maintain privacy without compromising functionality, because – unlike traditional encryption, which requires decrypting data for processing – FHE allows operations directly on encrypted data. 

For example, imagine a DeFi protocol that calculates how much a user can make by yield farming on their wallet. It would be desirable to keep the balances of the wallet private. Fully Homomorphic Encryption can run yield computations on encrypted wallet balances, keeping the user’s financial data private while still enabling the software to work with that data. Once the FHE computation has run, the output matches what the operation would have produced on plaintext data, but end-to-end security was maintained.

How FHE Works in Crypto

FHE works by using complex mathematical operations to encode data such that only authorized parties can decrypt and view results. Here’s how it applies to crypto-specific use cases:

1. Data Encryption: Wallet balances, transaction details, and smart contract states are encrypted using a public key.

2. Computation on Encrypted Data: The blockchain, or a decentralized application (dApp), performs necessary computations (e.g., token swaps, staking rewards) without decrypting the data.

3. Decryption of the output: The user or authorized party decrypts the result using their private key, revealing the final outcome (e.g., the amount of rewards earned).

This approach keeps all sensitive data encrypted during processing, ensuring no one—including validators or miners—can access private details.

How FHE Improves on zk-SNARKs and zk-STARKs

Zero-knowledge (zk) proofs, such as zk-SNARKs and zk-STARKs, have been instrumental in enhancing blockchain privacy. They allow users to prove the validity of a statement (e.g., ownership of funds) without revealing the underlying data. However, zk-proofs are limited to verification tasks and are not suited for general-purpose computations on encrypted data.

Fully Homomorphic Encryption takes privacy a level beyond zk-proofs by enabling arbitrary computations. Here’s how FHE improves on zk-tech:

• Generalized Computation: While zk-proofs specialize in verifying specific claims, FHE supports complex computations, such as executing encrypted smart contracts.
• Privacy Across Layers: FHE provides privacy for both on-chain and off-chain processes, whereas zk-proofs are primarily limited to specific use cases like transaction anonymity.
• Reduced Interactivity: zk-proofs often require interactive proof-generation, whereas FHE computations are non-interactive, making them more scalable for decentralized environments.

Benefits of FHE in Crypto

1. Privacy-Preserving Smart Contracts

Smart contracts are the backbone of DeFi, but their transparency can be a double-edged sword. FHE enables the execution of private smart contracts, where all inputs, states, and outputs remain encrypted. For instance, a private lending protocol could assess borrower creditworthiness without exposing sensitive financial data.

2. Confidential Transactions

While zk-proofs already allow for confidential transactions (e.g. Zcash, Tornado Cash), FHE expands this capability by enabling additional computations. For example, an FHE-based DeFi aggregator could send trades across multiple liquidity pools without unmasking the user.

3. Decentralized Identity (DID)

FHE can enhance decentralized identity systems because it can keep identity data encrypted, and still enabling verifiable computations on it. This ensures privacy during authentication processes: for example, for adult dApps, the system could verify that someone is over 18 without needing to know their date-of-birth or any other personal information.

4. Regulatory Compliance

With regulators increasingly scrutinizing the crypto industry, FHE allows platforms to provide compliance-ready solutions without sacrificing user privacy. For example, exchanges could perform anti-money laundering (AML) checks on encrypted user data, ensuring compliance while safeguarding user identities.

5. Secure Multi-Party Computation (MPC)

FHE simplifies secure multi-party computation, a process essential for activities like DAO voting and collaborative audits. Participants contribute encrypted inputs, and computations are performed on those without revealing individual input data.

Biggest FHE Projects for 2025

Zama: Bridging FHE and Blockchain

Zama is a trailblazer in bringing Fully Homomorphic Encryption to real-world applications, including blockchain and crypto. Their goal is to make FHE accessible to developers through optimized tools and libraries.

• Concrete Framework: Zama’s open-source ‘Concrete’ library simplifies the integration of FHE into decentralized applications. For example, developers can use this framework to create private smart contracts without needing advanced cryptography expertise.
• DeFi Use Cases: Zama is actively exploring how FHE can enhance privacy in DeFi. Imagine yield optimizers like Yearn Finance performing encrypted calculations to generate optimal returns without exposing user balances or strategies.
• Performance Optimization: Zama is addressing one of FHE’s biggest challenges—computation overhead—with hardware acceleration and mathematical optimizations.

Fhenix: Privacy Meets Scalability

Fhenix takes Fully Homomorphic Encryption a step further by applying it directly to blockchain architecture. Their mission is to create privacy-preserving, scalable solutions that address the main blockchain limitations.

• Encrypted Smart Contracts: Fhenix enables private smart contracts. Developers can use this to build dApps that process sensitive data securely. For example, a payroll dApp could compute salaries based on encrypted work hours without revealing employee data to the employer.
• Layer-2 Scalability: Fhenix uses FHE to help layer-2 scalability. Encrypted transactions are bundled and processed off-chain, reducing blockchain congestion while maintaining privacy.
• Privacy-First DAOs: By integrating FHE, Fhenix supports confidential DAO voting and decision-making processes, ensuring member privacy without sacrificing transparency.

The protocol has a ton of other use cases, such as MEV protection and blind auctions. 

Challenges and the Road Ahead

For all its promise, Fully Homomorphic Encryption still faces challenges that need to be addressed for widespread adoption in crypto:

1. Computation Overhead: FHE operations are resource-intensive, and can be slower than traditional methods. Zama and Fhenix are working to optimize performance, but further advancements are needed.
   
2. Key Management: Secure and user-friendly key management is critical for FHE adoption in crypto wallets and applications.
   
3. Interoperability: Standardization across different FHE schemes to ensure compatibility across a broad blockchain ecosystem.
   
4. Developer Adoption: Making it easy to integrate FHE tools into dApps is crucial for fostering adoption.

Conclusion

Fully Homomorphic Encryption represents a paradigm shift in crypto privacy and security. By enabling computations on encrypted data, FHE empowers developers to build complex privacy-preserving applications that were previously impossible. 

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