ZK Proofs vs FHE: Which Will Dominate?
The digital space is racing toward a future where data is both private and useful. Two cryptographic technologies, and , are at the forefront of this revolution. Both promise to protect sensitive information, but they approach the difficulty from diverse angles. The question extends beyond determining which is better to identify which one will capture the largest share of the growing privacy technology market.
Key Takeaways
- ZK proof is the dominant technology for blockchain scalability and verification, leading to massive adoption through ZK rollups.
- FHE is the only technology that allows general computation on encrypted data, making it essential for private smart contracts and secure cloud machine learning.
- The future of privacy will likely involve a synergy of the two technologies, where FHE performs a private computation and a ZK proof is used to verify the correctness of that computation.
Understanding the Core Technologies
ZK proofs enable one person to affirm that a piece of information is true without disclosing any other data. In blockchain applications, this means validating transactions without revealing sensitive information such as amounts or participants. Since the invention of this technology, significant variants have emerged; recent ones include zk-SNARKs and zk-STARKs, which offer diverse trade-offs between proof size, verification speed, and setup requirements.
FHE allows mathematical operations to be performed on encrypted data while retaining its secrecy. That is to say, you can share encrypted data with another party who can process that data without ever viewing the underlying information. FHE comes in three variants: partially homomorphic (either addition or multiplication), somewhat homomorphic (both with limitations), and fully homomorphic (unlimited operations of both types).
How They Function
ZK-proofs operate through a prover-verifier relationship. The process involves circuit creation (converting computation into arithmetic circuits), witness generation (processing private inputs), proof generation (creating cryptographic commitments), and verification (checking commitments without accessing private data). Modern implementations utilize complex mathematical structures, including elliptic curves and polynomial commitments.
FHE schemes are based on lattice mathematics and use “learning with errors” difficultys where messages are encrypted using noise. The workflow involves encryption (the data owner encrypts information), computation (where a third party performs operations on encrypted data), result delivery (encrypted results are sent back), and decryption (where the owner decrypts using their secret key). Modern FHE uses bootstrapping to refresh ciphertexts and enable unlimited computations.
Current Performance and Adoption
Institutional adoption has been transformative, with major banks including Deutsche Bank, Walmart, and HSBC leveraging ZKP for cross-chain settlements. As of November 2025, DeFi’s total value locked surged to $135.28 billion, driven partly by ZK-based rollups. The ZKsync Atlas Upgrade introduced a sequencer capable of processing up to 43,000 transactions per second with costs around $0.0001 per transfer. This represents a thousand-fold improvement over ETH’s base layer throughput.
FHE has historically lagged; however, it is catching up. Companies like Zama report speed gains exceeding 2,300 times since 2022, enabling around 230 transactions per second. By 2025,
| Feature | ZK proofs | FHE |
| Primary goal | Verify the correctness of a computation/statement. | Compute directly on encrypted data. |
| Data visibility | Input is plaintext (known to the prover), but the proof reveals nothing. | Data is encrypted (ciphertext) throughout the computation. |
| Blockchain role | Scalability (rollups), state transitions, proof of solvency. | Private smart contracts, confidential DeFi logic. |
| Performance status | High initial proof cost, but quick verification. | High computational overhead, but rapidly improving with hardware. |
Applications
ZK-proofs have found their strongest applications in blockchain scalability and privacy. The technology powers that process transactions off the main chain while maintaining security guarantees. Projects such as zkSync, StarkNet, and Polygon zkEVM are leading development, with layer-2 answers expected to process over 60% of transactions by 2025. Beyond scaling, ZK-proofs enable privacy coins, such as Zcash, and identity verification systems, where users can prove attributes without revealing personal information.
FHE excels in scenarios requiring computation on encrypted data from multiple sources. Key use cases include secure cloud computation, privacy-preserving data analysis, and machine learning on encrypted data. Healthcare represents a prime application area where researchers can analyze patient data without accessing individual medical records. FHE addresses a key limitation of ZK systems by enabling collaborative computation on private state, making it particularly valuable for supply chain applications where competitors must collaborate without exposing proprietary information.
Technical Challenges
ZK proofs face circuit complexity issues that can lead to long generation times. ZK rollups require substantial computational power, and creating efficient ZK circuits requires specialized cryptographic knowledge. Despite improvements in tools, the learning curve remains steep.
FHE’s main barriers are performance and efficiency. Computations remain intensive despite improvements, with ciphertexts significantly larger than plaintexts. A calculation taking milliseconds on plain data might require seconds or minutes on encrypted data. FHE also lacks a native way to prove correctness of execution, often requiring a combination with other techniques.
Integration and Future Outlook
Rather than direct competitors, ZK proofs and FHE increasingly work together. Hybrid architectures use FHE for computation on encrypted data and wrap the output in a ZK proof to guarantee correctness. This separates concerns with FHE handling secrecy from those with ZK ensuring correctness.
ZK proofs currently hold a commanding lead due to their superior performance and blockchain integration. The technology has matured rapidly and secured greater institutional adoption; however, FHE’s trajectory suggests stronger long-term growth. Hardware accelerators arriving in 2025 are expected to speed up FHE by a factor of 1000 or more, potentially closing the performance gap. As artificial intelligence demands explode and data privacy regulations tighten globally, FHE’s ability to enable secure AI training positions it for explosive growth.
Bottom Line
Neither technology will ultimately reign supreme in terms of privacy. On the back of superior performance and blockchain integration, ZK proofs have secured an ahead lead as the default choice for verifiable privacy for decentralized applications. However, FHE is poised to anchor secure cloud computing and artificial intelligence as the unique capability to compute on encrypted data rises with hardware improvements. The winning strategy will be understanding which tool fits their specific privacy requirements, or more likely, a hybrid architecture that leverages both technologies to provide comprehensive data protection.
