Confidential On-Chain Computation: A Framework for Practical Implementation with Fully Homomorphic Encryption

Part 1: Foundations of Fully Homomorphic Encryption for Blockchain

1.1. Introduction: The Blockchain Privacy Paradox and the Promise of FHE

Public blockchain networks, such as Ethereum, offer a revolutionary model for trustless, decentralized computation. Their core value propositions—verifiability, immutability, and transparency—are achieved through a globally shared, public ledger where every transaction and state transition is auditable by any participant.1 However, this radical transparency creates a fundamental conflict, often termed the “blockchain privacy paradox.” While essential for security and consensus, the public nature of the ledger is a significant barrier to mainstream and institutional adoption for any application involving sensitive data.3

In the current paradigm, every token transfer, wallet balance, smart contract interaction, and governance vote is exposed on a public record. This lack of confidentiality makes decentralized finance (DeFi) protocols vulnerable to predatory strategies like Maximal Extractable Value (MEV), where sophisticated actors exploit information in the public mempool to front-run or sandwich trades, eroding user value.6 It prevents institutions from engaging in on-chain finance, as their trading strategies and positions would be immediately visible to competitors.8 Furthermore, it renders applications like private voting or confidential supply chain management untenable, as the secrecy required for such processes is fundamentally incompatible with a transparent ledger.10

Traditional encryption methods offer an incomplete solution. They are highly effective at protecting data “at rest” (e.g., stored on a hard drive) and “in transit” (e.g., sent over a network).3 However, to perform any computation on this data, it must first be decrypted, exposing it “in use.” This critical vulnerability reintroduces a trusted third party—the entity performing the computation—and undermines the core principle of a trustless, decentralized system.3

Fully Homomorphic Encryption (FHE) presents a unique and powerful solution to this paradox. First conceptualized in 1978 and rendered plausible by Craig Gentry’s breakthrough in 2009, FHE is a cryptographic primitive that allows for arbitrary computations to be performed directly on encrypted data (ciphertexts) without needing to decrypt it first.13 Given an encryption of a message , denoted as , FHE enables a third party to compute  for any function , without ever learning the underlying message .12 This capability to protect data while it is “in use” makes FHE the so-called “holy grail” of cryptography, as it enables the creation of end-to-end encrypted services where data remains confidential throughout its entire lifecycle.11 When applied to blockchain, FHE offers the potential to transform a public ledger into a secure, trustless computer for private data, finally reconciling the need for verifiability with the demand for confidentiality.

 

1.2. A Comparative Analysis of Core FHE Schemes

 

The practical implementation of FHE on-chain is not monolithic; it relies on a family of distinct cryptographic schemes, each with specific strengths, weaknesses, and ideal use cases. The choice of scheme is a foundational architectural decision that profoundly impacts the functionality and performance of a confidential smart contract.18 Modern FHE schemes are predominantly constructed upon the hardness of the Learning With Errors (LWE) and Ring Learning with Errors (RLWE) problems, which are widely believed to be resistant to attacks from both classical and quantum computers, making them a future-proof foundation for on-chain privacy.17

 

BFV (Brakerski/Fan-Vercauteren) and BGV (Brakerski-Gentry-Vaikuntanathan)

 

The BFV and BGV schemes are the workhorses of exact computation in the FHE world. They are specifically designed to perform arithmetic operations on integers or elements of a finite field with perfect precision.18 This characteristic makes them indispensable for financial applications where even the smallest rounding error is unacceptable. Use cases such as updating encrypted token balances, calculating loan interest, or tallying votes require the deterministic and exact arithmetic that BFV and BGV provide.19

These schemes encode messages as polynomials and embed them within a larger polynomial ring for encryption. A carefully calibrated amount of “noise” is added during encryption to ensure security. Each homomorphic operation, particularly multiplication, causes this noise to grow. To manage this growth and prevent it from corrupting the underlying message, BFV and BGV employ a technique called modulus switching.13 This process effectively reduces the size of the ciphertext modulus, which in turn scales down the noise, allowing for a greater number of consecutive operations (i.e., a larger “multiplicative depth”). Performance analysis indicates that BFV often achieves shorter latencies for additions and multiplications when the required computational depth is greater than two.18 These schemes are widely implemented in leading open-source libraries such as Microsoft SEAL, PALISADE, and OpenFHE, providing a robust foundation for developers.18

 

CKKS (Cheon-Kim-Kim-Song)

 

In contrast to the exactness of BFV/BGV, the CKKS scheme is optimized for approximate arithmetic on real or complex numbers, which are represented as fixed-point numbers.18 Each homomorphic operation in CKKS introduces a small amount of precision error, which accumulates over the course of a computation. While this makes CKKS unsuitable for applications requiring perfect fidelity, such as financial ledgers, it is highly efficient for a different class of problems where approximate results are acceptable. These include privacy-preserving machine learning (e.g., running inference on an encrypted model), complex financial modeling, and scientific simulations.12

The noise management technique in CKKS is known as rescaling, which is conceptually analogous to the modulus switching in BFV and BGV.24 A key feature of CKKS is its native support for Single Instruction, Multiple Data (SIMD) operations. This allows a single ciphertext to encrypt a vector of values, and a single homomorphic operation can be applied to all elements of the vector in parallel, leading to significant performance gains for data-parallel tasks.18 Like BFV, CKKS is available in major libraries, with OpenFHE offering advanced implementations that include support for bootstrapping, a technique to refresh noisy ciphertexts for computations of arbitrary depth.25

 

TFHE (Torus Fully Homomorphic Encryption) / FHEW

 

The TFHE scheme (and its predecessor, FHEW) takes a fundamentally different approach. Instead of operating on integers or real numbers, TFHE is optimized for Boolean logic, evaluating functions gate-by-gate on individual encrypted bits.22 This makes it exceptionally well-suited for computations that involve comparisons (e.g., is balance > amount?), conditional logic (if-then-else), and the evaluation of arbitrary non-linear functions, which are common requirements in complex smart contract logic.19

The defining characteristic of TFHE is its remarkably fast bootstrapping mechanism. Because noise grows with every operation, FHE schemes require a way to “refresh” a ciphertext to reduce its noise level. In BFV and CKKS, bootstrapping is a computationally expensive procedure. In TFHE, however, bootstrapping is designed to be extremely fast (e.g., under 15 milliseconds on a standard CPU 18) and is performed after every single gate operation. This gate-by-gate refresh allows TFHE to evaluate circuits of arbitrary depth without succumbing to noise overflow.

A critical innovation within the TFHE ecosystem is programmable bootstrapping (PBS). This technique allows a lookup table (LUT) to be evaluated as part of the bootstrapping process itself. This means that any univariate function can be computed efficiently and homomorphically by simply encoding it as a LUT and applying PBS. This is a powerful tool for implementing complex activation functions in neural networks or custom logic in smart contracts.7 The leading implementations of TFHE are developed by Zama, through its high-performance Rust library TFHE-rs and its developer-friendly compiler framework, Concrete.19

The distinct capabilities of these schemes illustrate that building a confidential on-chain application is not a matter of simply “adding FHE.” It requires a deliberate architectural choice rooted in the computational demands of the use case. An application for private DeFi might need the exact integer arithmetic of BFV for balance updates, but it would also require the Boolean logic of TFHE to perform a confidential check on whether a user’s collateral is sufficient to cover a loan. This inherent tension between computational needs has driven the development of advanced tools that support scheme switching or high-level compilers that can abstract away these cryptographic decisions, signaling a maturation of the field from pure cryptography toward practical software engineering.25

Table 1: Comparative Analysis of BFV, CKKS, and TFHE Schemes

Feature	BFV / BGV	CKKS	TFHE / FHEW
Underlying Problem	Ring Learning With Errors (RLWE)	Ring Learning With Errors (RLWE)	Learning With Errors (LWE) / RLWE
Primary Data Type	Integers, Finite Field Elements	Real/Complex Numbers (Fixed-Point)	Booleans / Individual Bits
Precision	Exact / Lossless	Approximate / Lossy	Exact / Lossless
Key Operations	Addition, Subtraction, Multiplication	Vectorized Addition, Multiplication, Dot Products	Boolean Gates (NAND, OR, etc.), Comparisons, Look-up Tables
Noise Management	Modulus Switching	Rescaling	Gate-by-gate Bootstrapping
Bootstrapping	Slow and computationally intensive	Slow and computationally intensive	Very Fast (<15ms), performed after each gate
Ideal On-Chain Use Cases	Confidential token balances, private voting tallies, exact financial calculations	Privacy-preserving ML inference, complex financial modeling (where approximation is tolerable)	Conditional logic in smart contracts (e.g., loan eligibility checks), private AMM slippage checks, auctions, private governance

 

Part 2: Architectures for On-Chain FHE Implementation

 

Integrating the powerful but computationally demanding primitive of FHE into the resource-constrained and adversarial environment of a public blockchain presents a formidable engineering challenge. The path to practical implementation has required moving beyond naive on-chain execution and developing sophisticated off-chain architectures that balance confidentiality with the core blockchain requirements of low cost, acceptable latency, and verifiable integrity.

 

2.1. The On-Chain Computation Challenge: Gas, Latency, and Verifiability

 

A direct, native implementation of FHE operations within a blockchain’s virtual machine, such as the EVM, is currently infeasible due to three fundamental obstacles.

First, the gas problem arises from the immense computational overhead of FHE. Homomorphic operations are orders of magnitude more complex and slower than the simple integer arithmetic performed by native EVM opcodes.31 In an environment where every computational step incurs a gas fee, executing even a simple FHE-enabled smart contract would be prohibitively expensive, running contrary to the standard optimization practices that prioritize minimal computation and storage usage.33

Second, the latency problem stems from the slow execution speed of FHE computations, particularly those involving bootstrapping or a high multiplicative depth.12 Blockchains rely on relatively fast and deterministic transaction execution to maintain network consensus and throughput. Integrating slow FHE operations directly into the block validation process would drastically increase block times and reduce the overall capacity of the network, making it unsuitable for most real-time applications.19

Third, and most critically, is the verifiability problem. The very property that makes FHE useful—its malleability, which allows ciphertexts to be transformed—is also a security risk in a trustless setting. An FHE scheme, by itself, provides no guarantee that the correct function was applied to the encrypted data.20 A malicious validator could, for instance, substitute a homomorphic addition with a subtraction, producing a perfectly valid ciphertext that encrypts the wrong result. Without an additional mechanism to verify the integrity of the computation, the state of the blockchain could be undetectably corrupted, defeating the purpose of a decentralized ledger.38

 

2.2. The FHE Coprocessor Model: A Practical Off-Chain Architecture

 

The infeasibility of native on-chain execution has led to the convergence on an off-chain architectural pattern known as the FHE coprocessor model. This is the dominant approach currently employed by leading projects such as Zama and Inco.34 This architecture effectively outsources the heavy cryptographic lifting to a specialized network, preserving the L1 blockchain for what it does best: settlement and data availability.

The workflow of the coprocessor model is as follows:

1. A user encrypts their input data client-side and submits it in a transaction to a smart contract on a base L1 or L2 chain.
2. The smart contract does not execute the FHE computation itself. Instead, its logic is designed to emit an event that specifies the encrypted inputs and the intended homomorphic operation (e.g., FheAdd, FheMul).34
3. A decentralized network of specialized off-chain nodes, known as coprocessors, continuously listens for these events. Upon detecting a request, these nodes execute the computationally intensive FHE operation.
4. The resulting encrypted output is stored by the coprocessor network and can be submitted back to the L1 chain for final settlement or used as an input for subsequent confidential computations.40

This model offers significant advantages. By moving the computation off-chain, it dramatically reduces the gas costs for the user, making confidential transactions economically viable.19 Furthermore, because it interacts with the base chain via standard events, this architecture can be “bolted on” to any existing EVM-compatible blockchain without requiring any modifications to the core protocol or virtual machine.34

However, this design re-introduces a trust assumption: users must trust that the coprocessor network executes the correct computation. This trust is mitigated through several crypto-economic and cryptographic mechanisms. The network can be secured by a staking and slashing system, where coprocessors post collateral that is forfeited if they are proven to have acted maliciously.34 Verifiability is often achieved through an optimistic model, where the computation is assumed to be correct, but a challenge period allows any observer to re-execute the FHE operation and submit a fraud proof if they detect a discrepancy. This public verifiability is a core security feature of the Zama and Fhenix designs.27

 

2.3. FHE Rollups: Scaling Confidentiality on Layer 2

 

Building upon the off-chain execution model, the FHE rollup represents a more deeply integrated approach to scaling confidential computation. Pioneered by projects like Fhenix, this architecture embeds FHE capabilities directly into the framework of a Layer 2 (L2) rollup that settles on a base L1 like Ethereum.41

FHE rollups typically use an optimistic rollup structure. Transactions are executed confidentially on the L2 network, where the state remains encrypted using FHE. The rollup operator then batches these transactions and posts the compressed data to the L1. The state transition is assumed to be valid, but a challenge window is provided during which independent verifiers can scrutinize the posted data. If an invalid state transition is detected, a verifier can submit a fraud proof to the L1, which triggers a dispute resolution process to penalize the malicious operator and revert the incorrect state.41

This architecture elegantly combines the scalability benefits of rollups—which drastically reduce transaction costs by batching many transactions into a single L1 settlement—with the end-to-end data confidentiality provided by FHE.44 It aims to provide a seamless L2 experience where confidentiality is a native feature of the network, rather than an external service provided by a separate coprocessor network.

 

2.4. The Trifecta of On-Chain Privacy: FHE, ZKP, and MPC

 

The development of these off-chain architectures reveals a crucial reality: FHE alone is insufficient to build a secure, decentralized, and verifiable system for on-chain privacy. A robust, production-grade implementation requires a synergistic combination of three distinct cryptographic primitives, each addressing a different facet of the problem.45

• Fully Homomorphic Encryption (FHE) for Confidentiality: FHE remains the core engine for privacy. Its unique ability to perform arbitrary computations on encrypted data is what enables programmable privacy and, most importantly, the concept of a private shared state—an encrypted variable on-chain that can be updated by multiple parties without decryption.37
• Zero-Knowledge Proofs (ZKP) for Integrity: ZKPs provide the missing piece of verifiability. While FHE computes on private data, a ZKP can prove that the computation was performed correctly without revealing the data itself.37 In the context of an FHE coprocessor, a ZKP can be generated to attest that the off-chain node executed the exact function specified by the on-chain event. ZKPs can also be used by clients to prove that their encrypted inputs are well-formed—for example, proving they encrypted a vote for “Yes” or “No” (a 1 or 0) without revealing their choice, thus preventing invalid data from corrupting a computation.16 While powerful, the integration is challenging, as embedding FHE logic within ZK circuits can lead to enormous computational overhead.37
• Multi-Party Computation (MPC) for Decentralized Trust: MPC addresses the centralization risk associated with the system’s global decryption key. Rather than entrusting a single entity or node with the power to decrypt, the private key is generated in a distributed manner and sharded among a committee of independent nodes.50 To perform a decryption—such as revealing the final result of a vote or allowing a user to view their token balance—a threshold of these nodes must collaboratively participate in a cryptographic protocol.27 This use of MPC for threshold decryption ensures that no single node or small coalition can unilaterally compromise the system’s privacy, providing robust defense against censorship and data leakage.27

The evolution of on-chain FHE architectures demonstrates a clear trajectory. The initial, intractable problem of native on-chain execution forced a shift to off-chain models. This architectural pivot, in turn, created a new set of challenges centered on verifiability and decentralized trust. The solution to these secondary challenges lies in augmenting FHE with ZKPs and MPC. Consequently, the most viable and secure systems for on-chain privacy are not “FHE blockchains” in a narrow sense, but are sophisticated, multi-protocol stacks that carefully orchestrate these three primitives to deliver a holistic solution.

Table 2: Comparative Roles of FHE, ZKP, and MPC in On-Chain Privacy

 

Technology	Primary Role	Strength in On-Chain Context	Weakness / Challenge	Example Implementation
FHE	Confidential Computation	Enables private shared state and programmable privacy on multi-user data.	High computational cost; lacks native verifiability (malleable).	Computing on encrypted token balances in Zama’s fhEVM.
ZKP	Computational Integrity	Provides verifiable, succinct proofs of correct execution or valid inputs.	High proving cost; complex circuit design; not ideal for shared, mutable state.	Proving that an FHE coprocessor executed the correct function, or that a user encrypted a valid vote.27
MPC	Decentralized Key Management	Distributes trust by sharding the global decryption key, preventing single points of failure.	Requires communication overhead between parties; complex protocol design.	A committee of nodes performing threshold decryption to reveal the final tally of a confidential vote.48

 

Part 3: Practical Implementations and Use Cases

 

The theoretical foundations and architectural patterns of on-chain FHE are now being translated into tangible infrastructure and applications. A burgeoning ecosystem of projects is building the tools and platforms necessary for developers to create confidential smart contracts, enabling a new generation of use cases in DeFi, governance, and beyond. This section surveys the leading platforms and provides an in-depth analysis of their application to solve specific, real-world problems.

 

3.1. Ecosystem Survey: Leading Projects in On-Chain FHE

 

The landscape of on-chain FHE is currently led by a few key projects that are providing the foundational infrastructure for this new paradigm of confidential computing.19

• Zama: Zama is a pioneer in the space, focusing on making FHE accessible to blockchain developers.

• Technology: Their core technology is built around the TFHE scheme, which is implemented in their high-performance, open-source Rust library, TFHE-rs.19 Their flagship blockchain product is the fhEVM, a full-stack framework designed to integrate FHE into any EVM-compatible chain using the coprocessor architecture previously described.29
• Developer Experience: Zama prioritizes a simple developer experience. Developers can write smart contracts in standard Solidity and use special encrypted data types (e.g., euint8, ebool) to designate which parts of the contract’s state and logic should be confidential. The fhEVM toolchain handles the complex cryptographic operations in the background.6
• Applications: Zama’s technology is being used to build a wide range of applications, including confidential ERC-20 tokens, private DeFi protocols, secure on-chain payments, and private governance systems.39

• Fhenix: Fhenix is focused on creating a dedicated, high-performance environment for confidential smart contracts on Ethereum.

• Technology: Fhenix is developing the first FHE Rollup, a Layer 2 network that provides confidentiality-as-a-service to the Ethereum ecosystem.41 It is based on an optimistic rollup design, which allows it to inherit security from Ethereum while scaling computation off-chain.43
• Key Innovation: A major focus for Fhenix has been solving the performance bottleneck of threshold decryption. Their research has produced a novel MPC-based protocol that they claim achieves a throughput improvement of approximately 20,000x over previous state-of-the-art methods. This is a critical breakthrough for user-facing applications in DeFi, where low-latency decryption is essential for a good user experience.51
• Ecosystem: Fhenix aims to enable a full suite of confidential DeFi applications, including private Automated Market Makers (AMMs), confidential lending markets, and privacy-preserving Real World Asset (RWA) tokenization.53

• Inco Network: Inco positions itself as a universal, modular confidentiality layer for the entire Web3 ecosystem.

• Technology: Inco is an EVM-based Layer 1 blockchain that is secured by Ethereum through restaking via EigenLayer.21 It provides “Confidentiality-as-a-Service” that can be integrated with other blockchains.21
• Architecture: The platform is designed to abstract away the complexities of FHE, allowing developers to build confidential dApps using familiar tools like Solidity, Remix, and Metamask.54 Their stack utilizes the TFHE scheme in combination with MPC for key management.55
• Features: Inco’s core offerings include support for encrypted state on-chain, composable encrypted state transitions, and a source of verifiable on-chain randomness, which are crucial building blocks for private gaming and DeFi applications.21

Table 3: Overview of On-Chain FHE Platforms

Platform	Core Architecture	Primary FHE Scheme	Key Management	Verifiability Method	Target Chains	Developer Tooling
Zama	EVM Coprocessor	TFHE	MPC-based KMS	Optimistic (Public Re-computation)	Any EVM-compatible L1/L2	Solidity with encrypted data types (fhEVM)
Fhenix	Optimistic L2 Rollup	TFHE	MPC (for Threshold Decryption)	Optimistic (Fraud Proofs)	Ethereum (as an L2)	EVM-compatible languages and tools
Inco	Modular L1 (secured by Ethereum)	TFHE	MPC	ZK Proofs (for input validity)	Universal layer for any blockchain	Solidity and standard EVM toolchain

 

3.2. Private Decentralized Finance (DeFi)

 

DeFi is arguably the most compelling initial application area for on-chain FHE, as the need for financial privacy is acute and the potential market size is enormous.56

 

Confidential Transactions and Token Standards

 

The bedrock of any private financial system is the ability to conduct confidential transactions, hiding wallet balances and transfer amounts from public view.58 FHE enables the creation of confidential token standards, such as confidential ERC-20s. In this model, a standard token like USDC can be “wrapped” into an encrypted version (e.g., cUSDC). A user’s balance is stored on-chain as a ciphertext, and when they make a transfer, the smart contract performs a homomorphic subtraction on the sender’s encrypted balance and a homomorphic addition on the recipient’s, all without ever decrypting the values.6 This initiative is being spearheaded by the Confidential Token Association, a collaboration between Zama, Inco, OpenZeppelin, and other key players, to ensure interoperability and a common standard for private assets.19

 

Private Automated Market Makers (AMMs)

 

Standard AMMs like Uniswap, which rely on liquidity pools and deterministic pricing formulas, are a cornerstone of DeFi.61 However, their operation on a transparent ledger makes them highly vulnerable to MEV attacks. Bots constantly monitor the public mempool for large trades and can execute front-running or sandwich attacks to extract profit at the expense of ordinary users.7

FHE offers a powerful solution to this problem by enabling private AMMs. In such a system:

1. A trader encrypts their swap details (e.g., input token, output token, amount, and slippage tolerance) client-side.
2. The encrypted transaction is submitted to the private AMM smart contract.
3. The contract homomorphically processes the trade. It confidentially verifies the user has a sufficient encrypted balance, calculates the output amount based on the encrypted state of the liquidity pools, and performs checks like slippage tolerance using homomorphic comparisons.
4. The encrypted liquidity pool balances are updated, and the encrypted output token is sent to the trader.

Throughout this entire process, the details of the trade remain confidential, shielding the user from MEV bots and protecting their trading strategies from being copied.6 The TFHE scheme is particularly well-suited for this use case due to its efficient support for the comparison operations needed for slippage checks and other complex logic.7

 

Confidential Lending and Credit Scoring

 

A major limitation of current DeFi lending protocols is their reliance on over-collateralization. Because there is no on-chain identity or credit system, protocols must require users to lock up assets worth more than the value of their loan to mitigate default risk.5

FHE can bridge this gap by enabling uncollateralized or under-collateralized lending based on private credit scoring. A user could obtain a credit score from a reputable off-chain provider and submit an encrypted version of this score to a lending protocol’s smart contract. The contract could then homomorphically execute a check, such as is credit_score > 700?, to determine the user’s eligibility for a loan without ever learning the actual score.6 This allows for the creation of more capital-efficient lending markets that mirror the credit-based systems of traditional finance, while preserving user privacy.8 Key challenges remain in this area, such as designing effective liquidation mechanisms for positions with encrypted values and building secure oracles that can provide data to confidential smart contracts without leaking information.6

 

3.3. Confidential On-Chain Governance and Voting

 

On-chain governance, particularly through Decentralized Autonomous Organizations (DAOs), is a powerful tool for community-led decision-making. However, the public nature of on-chain voting creates significant problems. It exposes how each participant votes, making them susceptible to coercion, bribery, or social pressure, which can undermine the integrity of the democratic process.10 While blockchains provide excellent tamper-resistance, they do so at the cost of ballot secrecy.2

FHE provides an elegant solution for confidential voting. The process works as follows:

1. Encrypted Casting: Each eligible voter encrypts their vote. For a simple “Yes/No” poll, this could be Encrypt(1) for “Yes” and Encrypt(0) for “No”.10
2. Homomorphic Tallying: The voting smart contract receives all the encrypted ballots. It then uses the additive property of the FHE scheme to homomorphically sum all the ciphertexts. The result is a single ciphertext that encrypts the total number of “Yes” votes: Enc(vote_1) + Enc(vote_2) +… + Enc(vote_n) = Enc(total_yes_votes).19
3. Threshold Decryption: At the conclusion of the voting period, the final encrypted tally is decrypted. To prevent a single party from controlling this process, a committee of trustees uses an MPC protocol to perform a threshold decryption. Each trustee holds a share of the private key, and a quorum must collaborate to decrypt the result. This reveals only the final aggregate count, ensuring that individual votes remain secret throughout the entire process.19

To enhance security, this system can be combined with ZKPs. A voter can generate a ZKP to prove that their submitted ciphertext is an encryption of a valid vote (i.e., a 0 or a 1) without revealing which one it is. This prevents malicious actors from submitting encrypted invalid votes (e.g., Encrypt(100)) that would corrupt the final tally.27

 

3.4. Encrypted State Transitions: The Foundation of Private Shared State

 

The use cases in DeFi and governance all rely on a core technical capability that FHE uniquely enables on a blockchain: private shared state.34 This concept refers to a state variable—such as an AMM’s liquidity pool balance, a DAO’s treasury, or a game’s internal state—that is stored on-chain in an encrypted format and can be collaboratively and publicly updated by multiple, mutually untrusting users without ever being decrypted.

This capability marks a fundamental distinction between FHE and other privacy technologies like ZKPs. While ZKP-based systems like Zcash or Aztec are excellent at managing private state for a single user (e.g., a shielded balance), they are less suited for complex, multi-user interactions on a shared secret state. Modifying a shared state in a ZK system often requires revealing information to the parties involved in the state transition.

FHE, by contrast, allows smart contracts to perform state transitions directly on the encrypted data. This preserves not only confidentiality but also the composability that is the hallmark of DeFi. For example, a user’s single encrypted balance could be used as collateral in one private lending protocol and simultaneously be used to provide liquidity in a separate private AMM. Neither protocol, nor the public, would learn the value of the balance, yet the logic of both protocols could be correctly executed.6 This ability to build interconnected applications that operate on a shared, private state is a paradigm shift for blockchain development. The frameworks being built by Zama, Fhenix, and Inco are, at their core, platforms for securely managing and executing these encrypted state transitions, paving the way for a new class of dApps that were previously impossible on a transparent ledger.21

 

Part 4: Future Outlook and Recommendations

 

While Fully Homomorphic Encryption has made the leap from cryptographic theory to practical on-chain implementation, its journey toward widespread adoption is ongoing. The path forward requires surmounting significant hurdles in performance, usability, and standardization. For developers and protocol designers, navigating this evolving landscape demands a strategic, application-driven approach to harness FHE’s transformative potential.

 

4.1. Overcoming the Final Hurdles: Performance, Usability, and Standardization

 

The primary challenges that remain for on-chain FHE can be categorized into three key areas.

• Performance: The computational intensity of FHE remains its most significant bottleneck. While software-level optimizations and the adoption of efficient schemes like TFHE have yielded substantial improvements, current systems running on GPUs can achieve throughputs in the range of 20-50 transactions per second (TPS).21 While sufficient for many early-stage crypto use cases, this is far from the capacity needed for mass adoption. The next major leap in performance is expected to come from hardware acceleration. Several entities are actively developing FHE-specific Field-Programmable Gate Arrays (FPGAs) and Application-Specific Integrated Circuits (ASICs), which promise performance improvements of 100x to 1000x. These advancements are critical for achieving throughputs in the thousands of TPS, which would make confidential on-chain applications competitive with their transparent counterparts.27
• Usability: The inherent complexity of FHE cryptography presents a steep learning curve for developers. Choosing correct parameters, managing noise growth, and structuring programs in an “FHE-friendly” manner requires specialized expertise that most application developers do not possess.68 The future of FHE adoption hinges on improving the developer experience through high-level abstraction. The development of advanced compilers and transpilers is crucial. Tools like Zama’s Concrete, Sunscreen’s Rust-based compiler, and Google’s HEIR framework aim to automate complex cryptographic tasks like parameter selection, noise management, and circuit optimization. By allowing developers to write code in familiar languages like Python or Rust and simply marking data as “private,” these tools lower the barrier to entry and enable a much broader range of developers to build confidential applications.28
• Standardization: For a vibrant and composable ecosystem of private dApps to flourish, common standards are essential. Interoperability between different confidential applications requires a shared understanding of how encrypted data is represented and manipulated. The work of the Confidential Token Association, which includes Zama, Inco, and OpenZeppelin, to create a unified standard for confidential ERC-20 tokens is a critical first step in this direction. Such standards ensure that a confidential token created in one protocol can be seamlessly used in another, preserving the “money legos” ethos of DeFi in a private context.19

 

4.2. Strategic Recommendations for Developers and Protocols

 

Developers and protocol designers looking to build with FHE should adopt a strategic mindset that acknowledges both its power and its current limitations.

• Adopt an Application-Driven Design Philosophy: The central theme of this report is that there is no one-size-fits-all FHE solution. The choice of cryptographic scheme and system architecture must be dictated by the specific requirements of the application. Developers must ask critical questions at the outset: Does the application require the perfect precision of BFV for financial accounting? Does it need the efficient comparisons and conditional logic of TFHE for complex smart contract rules? Or can it tolerate the approximate arithmetic of CKKS for tasks like ML inference? Similarly, is the low latency of a direct coprocessor interaction essential, or can the application accommodate the finality delay of an optimistic rollup? A clear understanding of these trade-offs is paramount to building a successful and performant confidential application.19
• Design “FHE-Friendly” Smart Contracts: The performance of FHE is not uniform across all operations; in most schemes, multiplication is significantly more expensive than addition. Therefore, smart contract logic should be consciously designed to be “FHE-friendly.” This involves minimizing the number of homomorphic multiplications in a computation (i.e., reducing the multiplicative depth) wherever possible. This might require restructuring algorithms to favor additive operations or leveraging the unique capabilities of certain schemes, such as using TFHE’s programmable bootstrapping to evaluate a complex, non-linear function in a single, efficient step rather than decomposing it into a deep circuit of expensive multiplications.68
• Embrace the Hybrid Cryptographic Model: It is crucial to recognize that FHE, ZKP, and MPC are not competing technologies but complementary components of a complete on-chain privacy stack. Developers should not seek a single primitive to solve all problems but should instead architect solutions that leverage the unique strengths of each. A robust design will use FHE for its core competency of confidential computation on shared state, integrate ZKPs to provide verifiable integrity of those computations, and rely on MPC for decentralized and secure management of the system’s decryption keys. This hybrid model provides a defense-in-depth approach that addresses confidentiality, integrity, and decentralization simultaneously.45

 

4.3. Conclusion: The Dawn of a Confidential and Composable Web3

 

Fully Homomorphic Encryption is rapidly transitioning from a long-held theoretical aspiration to a practical, albeit challenging, technology for building the next generation of blockchain applications.19 The industry has moved past the initial, infeasible goal of native on-chain FHE execution and has converged on viable off-chain architectures, primarily FHE coprocessors and L2 rollups. This architectural shift, combined with the synergistic integration of Zero-Knowledge Proofs for integrity and Multi-Party Computation for decentralized trust, has forged a clear and credible path toward deploying secure and scalable confidential smart contracts.

The most profound impact of FHE on the blockchain ecosystem is its ability to enable private shared state. This new architectural paradigm unlocks a design space for multi-user, composable applications that were fundamentally impossible on a transparent ledger. From MEV-resistant DeFi protocols and private on-chain credit systems to secure and confidential governance, FHE provides the missing piece of the puzzle needed to solve the blockchain’s inherent privacy paradox.

As hardware acceleration continues to drive performance gains and high-level developer tools abstract away cryptographic complexity, FHE is poised to become a foundational layer of the Web3 stack. By finally reconciling the need for decentralized verifiability with the right to confidentiality, FHE will be instrumental in attracting the next wave of institutional capital and mainstream users to the blockchain. This evolution marks the beginning of a more mature, secure, and private Web3—one that moves closer to the vision of a truly end-to-end encrypted internet, or “HTTPZ,” where privacy is not an afterthought, but a guaranteed feature of the protocol itself.