Fluton x Fhenix: Confidentiality Meets Anonymity

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1. Context: Why This Matters

Quick Primer: What FHE Actually Accomplishes

Fully Homomorphic Encryption (FHE) represents a cryptographic breakthrough that allows computations to be performed directly on encrypted data without ever decrypting it. After years of theoretical development, FHE is finally reaching practical maturity, enabling real-world applications that were previously impossible. The technology has crossed a critical threshold in 2024-25, with bootstrapping latencies falling from seconds to single-digit milliseconds on modern accelerators.

Anonymity vs. Confidentiality:

These are distinct but complementary privacy properties that address different aspects of user protection. Confidentiality hides what you're doing, including transaction amounts, token types, and trading strategies. Anonymity hides who you are, protecting wallet addresses and preventing identity linkability across transactions.

Blockchain privacy requires both confidentiality and anonymity to achieve meaningful protection. While current architectures force trade-offs between transparency and privacy, emerging cryptographic solutions are enabling new paradigms where both institutional requirements and individual privacy can coexist. The continued development of these technologies is essential for institutional adoption and sophisticated DeFi strategies.

The Gap: Why Anonymity Remains Such a Challenge

Ethereum's core architecture creates fundamental anonymity challenges that have proven difficult to solve without breaking core functionality. Every transaction must originate from an identifiable address, creating permanent on-chain links that enable sophisticated tracking and analysis. This creates a composability versus privacy trade-off, where existing privacy solutions like mixers break DeFi composability by requiring funds to exit and re-enter protocols, disrupting the seamless interaction that makes DeFi valuable.

Even with confidential amounts, visible addresses enable targeted MEV attacks and strategy exploitation. Specialized attackers can analyze gas payments, timing patterns, and interaction sequences to reveal behavioral fingerprints that compromise user privacy. The metadata leakage problem extends beyond simple transaction analysis to include network-level tracking and cross-protocol correlation attacks.

2. Fluton's Design Choices

a. Confidentiality via FHE

Fluton leverages both Zama’s coprocessors and Fhenix’s coFHE to ensure that transaction details never appear in plaintext. Core transfers and bridge operations run through FHE coprocessors, keeping amounts, strategies, and cross-chain flows encrypted throughout execution. Zama’s fhEVM and Fhenix’s coFHE technology extends Solidity with encrypted types (euint), enabling confidential smart contracts on all EVM-compatible chains, while expanding soon to additional ecosystems beyond EVM.

When coordination between two different providers’ coprocessors is required, Fluton performs an intermediate decryption inside a TEE, ensuring that no sensitive data is exposed during the transition. This design allows encrypted states to move securely across heterogeneous infrastructures.

b. Anonymity via Smart Accounts

Fluton's anonymity layer operates through sophisticated account abstraction that builds on emerging infrastructure developments. On first frontend interaction, Fluton automatically creates a new smart account wallet per user, establishing fresh identities that cannot be linked to original externally owned accounts (EOAs). The smart account signs transactions while sending an encrypted version of the original wallet address to contracts, creating a privacy-preserving authentication mechanism.This approach enables completely unlinkable on-chain activity, where observers only see fresh smart account interactions without any connection to the user's original EOA.

c. Relayer / Solver Model

Fluton implements a dual-tier execution system optimized for different transaction sizes and complexity levels, incorporating lessons from advanced intent-centric architectures. The system includes Fluton Relayers that handle small to medium transfers with low fees, providing cost-effective anonymity for routine transactions. For larger, more complex operations, the Anoma Solver Network provides deep liquidity and multi-step confidential execution.

Anoma represents a pioneering approach to intent-centric blockchain architecture. Rather than requiring users to author low-level transactions, Anoma allows them to publish high-level intents (desired end-states) that a decentralized solver network processes. This approach naturally complements Fluton's privacy goals by abstracting away implementation details while maintaining user privacy.

The dynamic routing system intelligently chooses between execution paths, ensuring optimal efficiency while maintaining privacy guarantees. Gas abstraction capabilities allow solvers to sponsor gas fees on behalf of users, eliminating the need for gas tokens and preventing gas-payer metadata leaks that could compromise anonymity.

d. Adapter

The Confidentiality Adapter serves as Fluton’s middleware, ensuring that users can interact with existing DeFi protocols confidentially without requiring modifications to those protocols. Instead of exposing balances, trade sizes, or wallet activity, the adapter obfuscates all sensitive details through batched encryption and privacy-preserving relaying.

3. Deep Dive: How Fluton Achieves Anonymity + Confidentiality

A. The Rationale

Fluton's architecture addresses three core principles that represent the current state-of-the-art in privacy-preserving blockchain design. Confidential execution leverages FHE to enable contracts to compute directly on encrypted amounts and logic, building on years of cryptographic research that has finally reached practical maturity. The anonymity by default principle prevents raw wallet addresses from ever touching DeFi contracts directly, addressing a fundamental weakness in current blockchain privacy solutions.

Composability preservation maintains seamless integration with existing DeFi infrastructure, avoiding the significant limitations of mixer-based approaches that break the interconnected nature of DeFi protocols. The result creates an environment where observers see only fresh smart account activity unlinkable to EOAs, encrypted balances and transfer amounts, and solver/relayer cooperation behind an encrypted curtain.

B. The Integration (Overall Architecture)

Fluton's technology stack integrates four complementary layers that work together to provide comprehensive privacy protection.

FHE Coprocessors handle all confidential arithmetic off-chain in secure computing environments, performing direct computation on encrypted ciphertexts including balances, amounts, and addresses. The system outputs only usable results when conditions are met, maintaining unlinkability throughout the computation process.

The Smart Account Anonymity Layer automatically deploys lightweight account abstraction wallets for new users through the frontend, eliminating the manual complexity typically associated with privacy tools. Smart accounts transmit encrypted original wallet addresses to contracts, and contracts verify actions against encrypted addresses rather than raw ones, creating complete unlinkability between EOAs and on-chain activity.

The Relayers and Solvers layer provides differentiated execution paths optimized for different use cases. Fluton Relayers handle small and medium transfers with competitive fees, while Anoma Solvers provide deep liquidity for large, multi-step confidential execution. Dynamic selection ensures hidden user intent with efficient execution, and cross-chain coordination maintains privacy across networks through sophisticated cryptographic protocols.

The Confidentiality Adapter acts as a batching and encryption gateway between users and protocols, collecting encrypted inputs from multiple users including amounts, wallet ciphertexts, and order instructions. This component submits bundled transactions to downstream protocols while ensuring individual metadata is blurred within anonymity sets, providing protection against sophisticated analysis techniques.

C. Developer-Facing Building Blocks

The Fluton SDK provides developers with a modular toolkit for building privacy-centric, cross-chain dApps without requiring deep cryptographic expertise. Its architecture is composed of key components:

• FHE Coprocessor Integration via Zama’s production-ready infrastructure.
• Confidentiality Adapter for batching and encrypting protocol interactions.
• Solver Selection Logic that routes intents between Fluton Relayers and the Anoma network.
• AI Agent Infrastructure
• Smart Account Factory ****and ****Gas Abstraction

This developer-friendly approach makes advanced cryptography like FHE accessible to Ethereum developers through clear and practical abstractions.

D. The Impact

For users, Fluton enables on-chain trades and bridge transfers that become completely unlinkable to original wallets, while transaction amounts and strategies remain fully confidential throughout execution. This is achieved without any compromise on DeFi composability or user experience, representing a significant advancement over existing privacy solutions that require users to choose between privacy and functionality.

For DeFi protocols, integration requirements remain minimal with existing infrastructure, attracting institutional users who require both privacy and compliance capabilities. This enables new privacy-focused revenue models and use cases that were previously impossible due to regulatory and privacy concerns.

For Ethereum privacy research, Fluton demonstrates anonymity achievement without mixers or breaking composability, introducing encrypted addresses as homomorphically computed fields rather than plaintext strings. This creates new design space that could influence the broader development of Ethereum's privacy features.

For the broader ecosystem, Fluton elevates "private DeFi" standards beyond simple amount hiding, providing a working model that combines FHE, account abstraction, and relayer networks. This establishes a precedent for "fully confidential DeFi" implementations that could reshape expectations for privacy in decentralized finance.

4. Challenges and Trade-Offs

Technical Complexity

FHE computation requires significant computational resources, potentially increasing latency even with the latest hardware acceleration advances. While recent breakthroughs have reduced bootstrapping times from seconds to milliseconds on specialized hardware, the technology still imposes meaningful performance costs. The multi-layer architecture introduces additional points of failure and coordination complexity that must be carefully managed to maintain system reliability.

Cross-chain privacy maintenance requires cryptographic protocols that push the boundaries of current technology. The integration of multiple complex systems FHE coprocessors, account abstraction, solver networks, and cross-chain infrastructure creates challenging coordination problems that require careful engineering and extensive testing.

Economic Considerations

Higher gas costs for FHE operations may limit adoption for small transactions, creating potential accessibility issues for retail users. The relayer and solver incentive alignment is crucial for maintaining anonymity guarantees, requiring careful economic design to ensure that privacy protections remain strong even as the system scales. The computational costs of FHE operations, while decreasing rapidly, still represent a significant overhead compared to traditional blockchain transactions. Current estimates suggest that FHE operations require compute overhead of at least 1000× native performance even on specialized accelerators, though this is expected to improve dramatically as hardware and algorithms continue to advance.

Regulatory Uncertainty

Evolving privacy regulations may impact protocol development and adoption, particularly as regulatory frameworks struggle to keep pace with advancing cryptographic capabilities. Compliance requirements may necessitate optional auditability features that could complicate the privacy model. Institutional adoption remains dependent on regulatory clarity around the use of advanced privacy technologies in financial applications.

The question of whether FHE outputs are fully "anonymous" for GDPR compliance remains unresolved, with regulatory guidance still evolving. This uncertainty creates challenges for institutional adoption and may require protocol modifications to accommodate compliance requirements.

User Experience

Privacy benefits may not be immediately apparent to casual users who are accustomed to transparent blockchain operations. The educational overhead required for understanding the dual-privacy model represents a significant barrier to mainstream adoption.

The need to manage multiple accounts and understand complex privacy trade-offs adds cognitive load for users, potentially limiting adoption among less technically sophisticated user bases. Careful user experience design will be crucial for making advanced privacy features accessible to mainstream users.

5. Why This Is Unique

Beyond Traditional Mixers

Fluton's approach fundamentally differs from existing privacy solutions in several key ways. Prior anonymity attempts rely on mixers or shielded pools that break DeFi composability by requiring funds to exit and re-enter protocols,Fluton preserves composability while achieving anonymity through smart account abstraction, allowing users to maintain seamless interaction with existing protocols like Uniswap and Aave without sacrificing privacy.

This represents a significant advancement over projects like Tornado Cash, which provided anonymity at the cost of composability and regulatory compliance.

Dual-Privacy Innovation

Fluton represents the first practical implementation combining FHE confidentiality with anonymity in a production-ready system. This creates "fully confidential DeFi" where both transaction details and identity remain hidden throughout the entire execution process. This approach maintains cross-chain privacy without sacrificing speed or accessibility.

The integration of multiple technologies;  FHE for confidentiality, account abstraction for anonymity, and intent-based execution for composability represents a ground-breaking approach that goes beyond single-technology solutions. This multi-layered approach addresses the various ways that privacy can be compromised in blockchain systems.

Architectural Sophistication

The system leverages cutting-edge cryptography through Zama's FHE infrastructure while integrating practical account abstraction and solver networks. The integration of multiple infrastructure providers including Union for cross-chain capabilities, Anoma for intent-centric execution, and Zama for FHE computation creates a cohesive system that addresses the full spectrum of privacy concerns in DeFi.

The developer-friendly SDK enables privacy-preserving application development without requiring deep cryptographic expertise, potentially accelerating the adoption of privacy-preserving applications. This builds on the lessons learned from projects like Fhenix, which has demonstrated that complex cryptographic technologies can be made accessible to mainstream developers through careful abstraction and tooling.

6. Implications for Ethereum and Next Steps

Immediate Opportunities

Fluton enables anonymous yet auditable systems through optional solver audit trails, providing a path toward regulatory compliance without sacrificing privacy. The protocol demonstrates a dual-privacy model that could influence the broader development of Ethereum's account abstraction roadmap. This attracts institutional adoption by providing privacy guarantees that meet enterprise requirements while maintaining regulatory compliance options.

The success of Fluton could catalyze broader adoption of FHE technologies across the Ethereum ecosystem, building on the foundation laid by companies like Zama and Fhenix. As FHE performance continues to improve and costs decrease, we can expect to see more applications leveraging these privacy-preserving technologies.

Research Frontiers

Several critical research areas emerge from Fluton's approach that will shape the future of privacy-enabled blockchain systems:

• Confidential ERC-20 standardization becomes crucial as the ecosystem develops, requiring interoperable standards across different FHE providers while maintaining security and performance.
• Fair execution proofs present another challenge, demanding mechanisms to prove solver cooperation fairness without leaking private data that could compromise user privacy.
• Encrypted RPC endpoints represent a crucial infrastructure development, necessitating strategies to prevent metadata leaks at the networking layer that could undermine privacy protections.
• Cross-chain privacy protocols require complex development, with research focusing on how to maintain confidentiality across heterogeneous blockchain architectures with different security models and consensus mechanisms.

Fluton’s approach points to new research paths that will define how privacy can be standardized, proven, and extended across networks, shaping the future of confidentiality in blockchain.

Ecosystem Evolution

Fluton sets new baseline expectations for DeFi privacy, potentially making privacy features standard rather than exceptional in decentralized finance applications. This catalyzes development of privacy-first applications and protocols that build privacy considerations into their fundamental architecture rather than adding them as afterthoughts.

The influence on Ethereum's roadmap toward native privacy features could be significant, potentially accelerating the integration of privacy-preserving technologies into the base protocol. As the ecosystem matures, we can expect to see privacy features become as fundamental to blockchain infrastructure as consensus mechanisms and virtual machines.

Long-Term Vision

The ultimate vision encompasses universal confidentiality across all blockchain networks, enabled by continued advances in FHE performance and the development of standardized confidentiality enabled protocols. AI-driven private transaction optimization could enable privacy strategies that adapt to changing threat models and user requirements.

Seamless institutional integration with compliance-grade privacy represents a key goal, enabling enterprise adoption of blockchain technologies without compromising privacy or regulatory requirements. This could unlock significant new use cases and drive mainstream adoption.

Fluton represents more than a privacy solution: it's a blueprint for the future of confidential, anonymous, and composable DeFi. Fluton demonstrates that users don't have to choose between privacy and functionality. The future of blockchain is private, secure, and MEV-free—and Fluton, building on the foundational work of companies like Zama, Fhenix, Inco, is leading the way toward this privacy-preserving future.