Smart contracts have revolutionized decentralized computation, but privacy remains a critical challenge. While zero-knowledge proofs enable verifiable computation without revealing inputs, and fully homomorphic encryption (FHE) allows computations on encrypted data, each falls short of full programmability with privacy.

This article from SwapSpace CEO Andrew Wind explores how zk and FHE can coexist to form a powerful privacy-preserving execution layer. We examine their synergy, trade-offs, and the architectural patterns emerging to support programmable privacy in next-generation smart contracts.

Zero-knowledge proofs allow a prover to convince a verifier that a statement is true without revealing why it’s true. In blockchain systems, zk-SNARKs and zk-STARKs are used to prove that smart contract logic is executed correctly while keeping inputs and intermediate states private. These systems are highly efficient: proofs are small, verification is fast, and on-chain gas costs are low.

Example! In Tornado Cash, zk-SNARKs are used to prove that a user deposited ETH into the mixer contract, without revealing which deposit it was, enabling anonymous withdrawals with integrity guarantees.

However, zk systems typically require inputs to be committed before computation and don’t allow ongoing manipulation of encrypted state. They also rely on constraint-based programming models, which may require rewriting logic in domain-specific languages like Circom or Noir. Trusted setup (in SNARKs) and proof recursion complexity are additional considerations.

FHE allows arbitrary computation on encrypted data. A function can be applied directly to ciphertexts, and once decrypted, the output matches what it would have been on the plaintext. This means no information is leaked during execution — inputs, computation, and outputs all remain private.

Example! The ECG-PPS (Privacy Preserving Disease Diagnosis and Monitoring System for Real-Time ECG Signal) is a prototype that demonstrates how a healthcare dApp can compute diagnostic scores from encrypted medical records using FHE. This system performs real-time electrocardiogram (ECG) monitoring and analysis while ensuring patient data remains encrypted throughout the process. By leveraging FHE, ECG-PPS enables decentralized processing without revealing sensitive patient information, thereby preserving strict confidentiality.

Despite its power, FHE is heavy: ciphertexts are large, computations are slow, and toolchains are still maturing. It also lacks a native way to prove correctness of execution, which is critical in adversarial or decentralized environments.

• zk strengths: Lightweight proofs, efficient on-chain verification, verifiable computation.

• FHE strengths: Computation over encrypted inputs/outputs, ideal for privacy-sensitive data.

• zk limitations: Requires plaintext inputs, complex circuit modeling, and no encrypted state manipulation.

• FHE limitations: High computational cost, no native verifiability, and difficult on-chain integration.

When used together, zk and FHE can complement each other: zk provides verifiability, and FHE enables computation of encrypted data.

As decentralized applications grow more sophisticated, privacy needs extend beyond zero-knowledge proofs or homomorphic encryption alone. Modern smart contracts increasingly require both data secrecy and verifiable correctness, a dual demand that only a zk and FHE model can fulfill.

Neither technology alone can cover the full privacy surface area:

• Zero-Knowledge Proofs ensure the integrity of computation but assume plaintext access to inputs or require pre-commitments.

• Fully Homomorphic Encryption enables computation over encrypted data but lacks a mechanism to prove the computation was done correctly.

Their coexistence enables privacy-preserving smart contracts that are both trustless and confidential. Consider the following use cases:

1. Private DeFi protocols: Execute trades over encrypted balances or preferences using FHE, while using zk to prove that trades comply with protocol rules, without leaking user strategy or state.

2. Identity and reputation systems: Use zk to prove credentials (e.g., citizenship, credit score tier) without revealing full documents, while applying FHE to compute aggregated or personalized data privately (e.g., KYC risk assessments, social graphs).

3. Private Machine Learning (on-chain or off-chain): Run inference on encrypted user data via FHE, then use zk to prove correct model execution without exposing model weights or sensitive inputs.

Together, zk and FHE enable selective disclosure, encrypted state transitions, and private, verifiable logic, which are essential features for the next generation of secure, decentralized applications.

While zk and FHE serve different privacy functions, they can be composed in layered or integrated architectures to achieve both encrypted computation and verifiable execution. Let’s pay attention to the main coexistence patterns emerging in research and protocol design.

This architecture uses FHE to perform computation on encrypted data and then wraps the output or correctness proof in a zk proof. This pattern separates concerns: FHE handles secrecy, while zk guarantees correctness.

Example! A smart contract processes an FHE-encrypted transaction off-chain. The result is decrypted, and a zk-SNARK is generated attesting to the correct computation over the encrypted input, which is then submitted on-chain for verification.

• Pros: Modular; uses mature zk tooling for proof verification.

• Cons: Requires off-chain compute; introduces latency from dual cryptographic operations.

Some experimental designs allow FHE operations to be expressed inside zk circuits, treating FHE logic as part of the proof constraints. This allows developers to prove that FHE decryption or homomorphic functions were correctly executed.

Example! A Circom or Halo2 circuit verifies the correct application of a bootstrapped TFHE gate on encrypted data.

• Pros: Fully on-chain-compatible, single-proof system.

• Cons: Enormous circuit sizes; proving time and gas costs can be impractical with current tech.

In this model, a trusted execution environment (e.g., Intel SGX) runs FHE computation and outputs a zk proof certifying that it followed the correct logic without tampering. The zk proof is then verified on-chain.

Example! A decentralized oracle service computes a result over FHE-encrypted data inside SGX and uses a zk-STARK to attest correctness to smart contracts.

• Pros: Practical today; reduces cryptographic overhead.

• Cons: TEE trust assumptions; risk of hardware compromise.

A promising direction is integrating FHE into zkRollups. User states (balances, preferences) are stored encrypted with FHE, while zk-SNARKs prove valid state transitions. This enables private execution within scalable L2 environments.

Example! A private AMM rollup where orders and liquidity pools are encrypted, and each batch includes a zk proof that all encrypted trades obeyed protocol constraints.

• Pros: Scalable, composable, L2-native.

• Cons: Requires FHE-optimized rollup circuits; ecosystem is still nascent.

Each of these models reflects a different tradeoff between performance, privacy guarantees, and deployment complexity. As FHE libraries become faster and zk circuits more expressive, hybrid architectures will become increasingly practical for real-world deployment.

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Despite the promising synergy between zk and FHE, integrating them into a cohesive, performant architecture introduces several significant technical challenges:

• Circuit complexity and size explosion. Embedding FHE operations, especially bootstrapping or programmable gates, within zk circuits can lead to massive constraint systems. Even simple FHE functions require thousands of low-level operations, ballooning proving time and gas costs.

• Inefficient DSL tooling. Most zk development frameworks (e.g., Circom, Noir, Halo2) are not optimized for expressing FHE logic. FHE-friendly arithmetic (modulo operations, bit-level gates) is difficult to represent efficiently in zk circuits designed for field arithmetic.

• Key management complexity. FHE schemes typically rely on public evaluation keys and private decryption keys. When combined with zk commitments or Merkle proofs, securely coordinating key distribution across multiple parties (or contracts) introduces nontrivial trust assumptions and orchestration overhead.

• Latency and cost of dual proof systems. Running FHE computation followed by zk attestation often means two separate cryptographic pipelines. This increases latency and makes real-time use cases, like encrypted trading or private gaming, hard to achieve without aggressive optimization.

• Composable state and multi-party privacy. Maintaining encrypted state across transactions or users (e.g., FHE-based balances) requires homomorphic updates and possibly multi-key FHE, which is still an active area of research. Integrating this into composable, zk-verified smart contract logic remains unsolved at scale.

A growing ecosystem of protocols and research groups is actively exploring how zk and FHE can interoperate, pushing the boundaries of programmable privacy:

• Zama is pioneering practical FHE for blockchain through its TFHE-based library, Concrete, and has published blueprints for zk+FHE hybrid models. They’ve also proposed FHE rollups where FHE-encrypted state is verified via zk-SNARKs.

• Mina protocol natively supports zk-SNARKs on-chain and is investigating FHE integration to enable encrypted off-chain data processing with verifiable proofs submitted to its lightweight chain.

• Modulus Labs is developing zkML frameworks where machine learning inference occurs off-chain and is verified on-chain using zk-SNARKs, with interest in using FHE to preserve data secrecy during training or inference (Modulus Whitepaper).

• RISC Zero and Succinct are building zkVMs that could potentially support hybrid zk+FHE proof systems, allowing developers to write encrypted logic in general-purpose languages like Rust.

• Academic research from institutions like UC Berkeley and EPFL explores compiling FHE into zk circuits efficiently, secure MPC + FHE + zk protocols, and composable encrypted smart contract architectures.

These projects signal a convergence: the zk/FHE stack is maturing, and practical hybrid privacy systems are now within reach.

The convergence of zk and FHE signals a pivotal evolution in how we think about privacy, not as a bolt-on feature, but as a programmable primitive. Just as smart contracts made computation composable, the fusion of zk and FHE can make privacy composable, enabling encrypted state, private logic, and verifiable guarantees as first-class citizens in decentralized systems.

In the near term, we can expect:

• Specialized runtimes that abstract away the zk/FHE complexity, allowing developers to write private contracts in high-level languages.

• FHE-friendly rollups with zk attestations for encrypted state transitions.

• Toolchain integration, where compilers and VMs support hybrid privacy logic natively (e.g., zkVMs with FHE hooks).

Longer term, programmable privacy will underpin critical applications: decentralized AI, encrypted DAOs, anonymous voting, and private data marketplaces, all verified on-chain without exposing sensitive data.

The ultimate vision is a privacy layer that is as trustless, composable, and efficient as current L1 smart contract platforms. As zk and FHE continue to co-evolve, their integration will not just enable better privacy — it will redefine what kinds of decentralized systems are possible. The infrastructure is early, but the direction is inevitable.

The coexistence of zk and FHE unlocks a new approach where privacy and verifiability no longer compete but complement each other. This collaboration is essential for building truly private, programmable smart contracts capable of complex encrypted computation with trustless proofs. As the technology matures, combining these cryptographic tools will become foundational, leading to decentralized systems that safeguard user data without sacrificing transparency or security.