Encrypted Lending on Ethereum: How FHE Enables Private, MEV-Safe DeFi

How Fhenix FHE coprocessor (CoFHE) enables MEV-resistant, confidential lending protocols with full Ethereum composability

The Missing Primitive in Decentralized Finance

Decentralized finance has evolved through distinct technological breakthroughs. Automated market makers enabled DeFi Summer in 2020. Liquid staking tokens powered the 2023-2024 growth cycle. Each era introduced capabilities that expanded who could participate and what could be built onchain.

The next evolution centers on confidentiality. As institutional capital evaluates blockchain infrastructure, the transparent-by-default nature of current lending protocols creates fundamental barriers to adoption. Every position, every collateral amount, every liquidation threshold remains visible to competitors, arbitrageurs, and the broader market.

Fully Homomorphic Encryption (FHE) is a cryptographic scheme that allows operations on encrypted data without ever decrypting it, and can be used as the technological foundation for private decentralized finance. In the first half of 2025, Fhenix demonstrated that FHE-based encrypted computation operates at practical scale on Ethereum today - not as a theoretical framework, but as production-ready infrastructure.

FHE has historically faced performance limitations that restricted its practical applications. Fhenix addresses these challenges through a comprehensive approach combining multiple optimization techniques.

We build CoFHE, an FHE-based CoProcessor - in simple words it works like an engine that handles requests for private computations submitted by different dApps. CoFHE's architecture provides a dedicated encrypted execution infrastructure purpose-built for high-throughput workloads. Key technical approaches include:

• Parallelized threshold decryption enables validators to jointly decrypt results with low latency and distributed trust
• Optimized ciphertext structures reduce memory overhead and accelerate common FHE operations across the system
• An async model that allows calculations to run without delaying the host chain operation, helping to bypass computation latency

Our CCS 2025 research paper, "High-Throughput Universally Composable Threshold FHE Decryption" (ePrint 2025/1781), presents empirical measurements showing the viability of encrypted smart contracts for real-world applications:

• 20,000 decryptions per second
• Approximately 20,000× throughput improvement over prior FHE baselines
• Up to 37× improvement in latency over prior FHE baselines

These measurements represent the performance characteristics observed under controlled test conditions and demonstrate the technical feasibility of encrypted smart contracts for demanding applications like lending protocols. As with any emerging technology, production performance will vary based on specific implementation details, network conditions, and workload characteristics.

These benchmarks demonstrate that privacy-preserving computation can support the throughput and latency requirements of modern DeFi protocols.

Guy Itzhaki, CEO of Fhenix, explains the shift:

"The next wave of DeFi needs privacy by default. In H1 2025, we showed that encrypted compute is no longer theoretical - it's live, scalable, and composable. Developers now have the foundation to build truly Confidential DeFi."

Why Lending Protocols Need Confidentiality

Current Ethereum lending infrastructure - including protocols like Aave, Compound, Morpho, and Spark - operates with complete transparency. While transparency serves important verification purposes, it exposes critical vulnerabilities that prevent institutional adoption:

• Wallet balances and debt positions  - Collateral amounts are visible to all market participants, enabling targeted liquidation strategies and position monitoring
• Borrowing demand - Borrower positions can be tracked across protocols or tokens, revealing wallet-level risk profiles and capital deployment strategies
• Liquidation thresholds are public information, allowing sophisticated actors to time market movements for maximum extraction
• Competitive alpha - is exposed signaling the market regarding the trader strategy
• Yield strategies deployed by institutions can be reverse-engineered and front-run by competitors

This transparency enables several harmful dynamics:

• Liquidation sniping occurs when observers monitor positions approaching liquidation thresholds and execute transactions at precisely calibrated moments
• MEV extraction systematically captures value from borrowers through transaction ordering
• Market surveillance allows competitors to profile strategies and adjust their own positions accordingly
• Capital inefficiency forces sophisticated participants to account for information leakage in their position sizing

As institutions evaluate onchain lending, these transparency issues create compliance and competitive concerns that override technical benefits. Guy Zyskind, Founder of Fhenix, emphasizes the fundamental nature of this challenge:

"If DeFi remains transparent by default, it will never serve institutional scale. Confidentiality is not optional - it's fundamental."

Fhenix addresses these limitations by enabling developers to build fully encrypted lending protocols that preserve Ethereum's core strengths while adding confidential computation.

Building Encrypted Lending Protocols on Ethereum and EVM Chains

Fhenix provides infrastructure for deploying confidential lending protocols directly on Ethereum and any other EVM chains without compromising user experience or composability. Developers can launch encrypted applications while maintaining compatibility with:

• Standard wallet infrastructure including MetaMask, Ledger, and Rabby - users interact with encrypted protocols through familiar interfaces without additional setup or specialized software
• EVM equivalence ensures that encrypted contracts integrate seamlessly with existing Ethereum tooling, development workflows, and deployment infrastructure
• Composability enables running confidential logic within smart contracts, meaning builders can build any dApp they want with privacy built in
• Open-source development tooling provides familiar frameworks and libraries, reducing the learning curve for developers building confidential applications

With that in mind, lending protocols could integrate with Fhenix's stack to decide which components or data should remain private, such as:

• Keep borrower balances encrypted throughout their lifecycle to prevent liquidation sniping
• Encrypt collateral values so they never appear as plaintext onchain
• Keep the LTV (Loan-to-Value) ratio confidential and encrypt the liquidation logic using FHE functions to compute over the encrypted data
• Proof-of-reserves and protocol-level solvency remain publicly verifiable through threshold decryption mechanisms that selectively reveal aggregate data without compromising individual positions

The CCS 2025 paper details this architectural approach:

"Fhenix enables encrypted state, encrypted execution, and encrypted transactions through the CoFHE coprocessor while maintaining strict EVM equivalence."

This design allows lending protocols to operate with institutional-grade confidentiality while preserving the verification properties that make public blockchains trustworthy.

How Fhenix can unlock new value - Encrypted Lending Architecture

Fhenix enables smart contracts to compute on encrypted data, without ever revealing it. This preserves privacy while maintaining Ethereum compatibility and changes what’s possible in lending and credit:

Encrypted Lending Use Cases:

1. Confidential Collateral Positions
   ‍All sensitive protocol data exists as FHE ciphertexts rather than plaintext. Users can borrow without revealing how much they’ve posted or withdrawn as collateral. This encrypted state remains opaque to observers while remaining computationally accessible to the protocol itself.
2. Shielded Loan Terms
   ‍Lenders and borrowers negotiate terms (rates, durations, caps) privately, avoiding front-running or copycat strategies.
3. User credit score
   ‍Calculating a credit score that reflects a user’s financial activity using encrypted logic, while keeping the score private, can enable a lending platform to offer better lending rates based on the user’s score, giving the platform a competitive edge
4. Private Liquidation Thresholds
   ‍Liquidations can be triggered by encrypted logic, minimizing attack vectors and stress visibility.
5. DAO or Treasury Lending with Hidden Exposure
   ‍Institutions can lend without disclosing how much risk they’re taking, or in which markets.
6. Sealed Bids for liquidation
   ‍Fairness of who gets to liquidate - Replace mempool races with sealed commitments (commit–reveal, private relays) or builder/solver auctions that accept encrypted bids and settle atomically. That prevents gas wars and uncle/MEV shenanigans seen in Maker’s Black Thursday. (Maker moved away from the 2020 parameters after the incident, but the lesson stands.)
7. Selective Transparency via Threshold Decryption
   ‍While individual positions remain private, protocol-level metrics can be selectively revealed through threshold decryption mechanisms:
   • Proof-of-reserves becomes publicly verifiable
   • Total value locked can be disclosed for transparency
   • Aggregate utilization rates provide market insight
   • Individual user data remains encrypted and private
   This approach satisfies regulatory and transparency requirements while preserving user privacy. The CCS 2025 paper discusses this pattern in the section "Selective Transparency via Threshold Keys."

High-Demand Encrypted Lending Use Cases (Based on Market Research):

Fhenix product-market research shows that developers, institutions, and risk teams consistently request privacy-preserving DeFi primitives.

These use cases represent the most in-demand applications for encrypted lending on Ethereum and EVM chains, enabled by Fully Homomorphic Encryption (FHE) and the CoFHE coprocessor.

1. Encrypted Per-Account Collateral Balances (Private Collateral Positions)
   Confidential collateral accounting is the top requirement for private lending protocols.With encrypted collateral values, FHE prevents public visibility into who is close to liquidation - removing the “liquidation hit list” that MEV bots scrape today.Solvency remains verifiable through zk proofs of Health Factor (HF ≥ 1) without revealing raw numbers.This is the foundation for MEV-resistant lending and institutional DeFi privacy.
2. Encrypted Per-Account Debt Balances (Private Borrowing Exposure)
   ‍Debt values encrypted under FHE smart contracts prevent adversaries from inferring leverage, monitoring repayment patterns, or manipulating markets to force liquidation.Instead, protocols reveal only proofs like:“debt ≤ max borrow under risk parameters”.This pattern is essential for confidential DeFi lending and institutional risk visibility.
3. Encrypted Per-Account Health Factor (Private Liquidation State)
   ‍Rather than publishing exact Health Factors, protocols can provide binary zk proofs:HF ≥ 1 → safe (not liquidatable)HF < 1 → liquidatableThis removes precision targeting and eliminates MEV bots timing liquidations - one of the most requested features for private liquidation systems.
4. Sealed Liquidation Bids and Pricing Intent (Encrypted Auctions for Fair Liquidations)
   ‍Liquidations operate via sealed bids, commit–reveal flows, or builder-routed encrypted bundles - preventing front-run, gas wars, copycat bidding, and ordering manipulation.
5. Private Rescue Orderflow (Encrypted Top-Ups & Repayments)
   ‍“Save my position” actions route through private orderflow so distressed positions aren’t exposed.Prevents sandwiching, griefing, and MEV sniping during top-ups or repayments.
6. Per-Account Collateral Composition (Private Asset Mix)
   ‍Even if collateral amounts are hidden, revealing which assets a user has posted exposes oracle-manipulation vectors.Example:If an attacker sees a wallet heavily collateralized by a thin LP token or a low-liquidity ERC-4626 asset, they can manipulate inputs/oracles to push that asset into liquidation range.Fhenix enables collateral composition to remain fully encrypted while still publishing proofs that a user meets all basket-level requirements.This raises the attacker’s cost and complexity dramatically.
7. Pending Liquidation Queue & State Transitions (Encrypted Lifecycle Management)
   ‍DeFi liquidations often rely on highly visible queueing (who is next, when eligibility triggers, timestamps, block ordering).This transparency enables:
   • last-block sniping
   • ordering games
   • premature frontrunning of at-risk borrowers
   With FHE, protocols can hide per-address queue positions and timing, publishing only:liquidation eligibility and auction state (e.g., active Dutch auction stage).Pairing this with Dutch-style price discovery replaces speed races with fair liquidation mechanics while keeping distressed users protected.

Standard Ethereum User Experience

Despite the underlying cryptographic complexity, the user and developer experience remains familiar:

• End users interact through standard Ethereum wallets (MetaMask, Ledger, etc.) without modification
• Developers continue writing Solidity smart contracts using familiar patterns and tooling
• No circuit design or specialized cryptographic knowledge required
• Minimal changes to existing smart contract development workflows

For developers building encrypted lending protocols, Fhenix provides Solidity APIs that abstract cryptographic complexity. Implementation requires only:

1. Replace sensitive fields with encrypted datatypes like euint instead of standard Solidity integers
2. Use library functions such as FHE.add, FHE.gt, and FHE.mul for operations on encrypted values
3. Deploy and test using standard Ethereum development tools

The Fhenix Solidity API documentation provides comprehensive reference material for encrypted smart contract development. The quick start guide includes a complete starter kit for developing, testing, and deploying FHE contracts both locally and on test networks.

Encrypted DeFi Applications Beyond Lending

While lending represents the first major use case for confidential DeFi, the same FHE infrastructure enables privacy across other DeFi primitives.

Encrypted ERC20 Tokens

Privacy-preserving token implementations that maintain fungibility and composability:

• Balances remain hidden from all parties
• Transfer amounts stay encrypted onchain
• Users verify their own balance through client-side decryption
• Observers see only encrypted state

MEV-Proof Liquidation Systems

Confidential position monitoring without information leakage:

• Monitor encrypted health factors continuously
• Trigger liquidations based on encrypted thresholds
• Execute without revealing position details until completion
• Eliminate liquidation sniping that extracts value in transparent protocols

Encrypted Yield Strategies

Institutional-grade strategy deployment with competitive protection:

• Deploy capital according to proprietary strategies
• Conceal positions and targets from competitors
• Hide rebalancing logic and timing
• Maintain verification properties of public blockchains

Private Vaults and Collateral Management

Treasury management with traditional finance confidentiality standards:

• Manage positions with enterprise-grade privacy
• Execute complex collateral strategies without exposure
• Meet compliance requirements through selective transparency
• Preserve verification through threshold decryption

These applications share a common foundation: encrypted state, encrypted execution, and selective transparency where needed for verification or compliance.

The Infrastructure Opportunity for DeFi Developers

The performance milestones presented in “High-Throughput Universally Composable Threshold FHE Decryption” (CCS 2025) demonstrate that the technical barriers to Confidential DeFi have been addressed. FHE computation operates at speeds that support:

• Real-time liquidation systems
• High-frequency risk monitoring
• Interactive user experiences
• Production-scale throughput requirements

This creates immediate opportunities for developers to build confidential primitives that expand DeFi's addressable market:

Private Lending Markets

Offer institutional participants the confidentiality required for compliance while maintaining transparency needed for protocol security and solvency verification.

Encrypted Automated Market Makers (AMM)

Eliminate MEV extraction from liquidity provision and protect traders from sandwich attacks and information leakage.

Confidential Yield Aggregators

Execute sophisticated strategies without revealing positions, enabling competitive advantage through privacy rather than information asymmetry.

Privacy-Preserving Risk Management

Allow protocols to assess systemic risk without exposing individual user positions.

Institutional-Grade Settlement Layers

Bring traditional finance capital onchain without compromising the confidentiality standards expected in regulated markets.

The developers who build these confidential primitives will define the infrastructure layer for the next evolution of decentralized finance. Fhenix provides:

• The cryptographic foundation for encrypted computation via CoFHE
• Developer-friendly tooling that abstracts complexity
• Full Ethereum compatibility for seamless integration
• Testnet-ready infrastructure available today

Confidential DeFi as Ethereum's Privacy Layer

Fhenix delivers the infrastructure for encrypted lending on Ethereum at production scale. The platform provides:

• Encrypted EVM for confidential smart contracts
• The first dedicated FHE coprocessor for blockchain applications
• Empirical measurements showing approximately 20,000× throughput improvements over prior FHE baselines
• Real encrypted DeFi demonstrations including ERC20 payments, AMMs, and lending prototypes
• Full compatibility with Ethereum's user experience, development tooling, and composability model

Developers can build privacy-preserving protocols without fragmenting liquidity, forcing wallet migrations, or abandoning the Ethereum ecosystem's established network effects.

Confidential lending represents the beginning of a broader shift toward privacy-preserving decentralized finance. As institutional capital evaluates blockchain infrastructure, confidentiality moves from optional feature to fundamental requirement. The protocols that provide both transparency where needed for verification and privacy where needed for competition will capture the institutional market.

Encrypted DeFi infrastructure test net is live, scalable, and ready for deployment. The opportunity exists now for developers to build the confidential primitives that bring institutional capital onchain and unlock the next trillion dollars in DeFi.

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