Private AMM

Hides all sensitive transaction details from the public blockchain. This includes:

• The exact swap amount and token types involved.
• The resulting execution price and slippage.
• The updated state of liquidity pool reserves. Only the interacting parties and any necessary validators can see the full details, protecting against front-running and information leakage.

Uses zero-knowledge proofs (zk-SNARKs or zk-STARKs) to cryptographically verify that a swap is valid without revealing its inputs. The proof confirms that:

• The trade obeys the pool's constant product formula (x * y = k).
• All required fees have been paid.
• The user has sufficient balance. This allows the network to enforce correctness while maintaining complete privacy for the trade data.

Significantly mitigates Miner Extractable Value (MEV) by obfuscating transaction intent. Since trade sizes and directions are hidden, opportunistic actors like searchers and front-running bots cannot profitably exploit pending transactions. This creates a fairer trading environment and reduces costs for end users by eliminating sandwich attacks and priority gas auctions.

Enables users or protocols to prove specific facts about a private transaction without revealing the underlying data. For example, a user could generate a proof to a lending protocol showing they executed a trade of a certain minimum value to qualify for an incentive, or an auditor could verify aggregate protocol fees without seeing individual trades. This balances privacy with necessary compliance and accounting.

Protects the strategic information of liquidity providers (LPs). In a public AMM, anyone can monitor pool reserves to copy trading strategies or target large positions. A private AMM encrypts the pool's reserve balances, hiding:

• The total value locked (TVL) and its composition.
• The size of individual LP positions.
• The exact fee accumulation, protecting LP competitive advantage.

Some implementations use hardware-based Trusted Execution Environments (like Intel SGX) as an alternative to full ZK proofs. The swap logic executes inside a secure, isolated enclave on a validator's machine, which attests to the correctness of the result. This can offer higher computational efficiency for complex trades but introduces different trust assumptions regarding hardware integrity and operator honesty.

The foundational component of a private AMM is a shielded pool (or commitment pool). Instead of public ERC-20 balances, the pool holds encrypted commitments. Swaps are proven valid via zero-knowledge proofs without revealing:

• The input/output amounts of a specific trade.
• The user's wallet balance or identity.
• The real-time composition of the liquidity pool. This transforms the typical constant product formula x * y = k into a private computation.

A primary design goal for private AMMs is mitigating Maximal Extractable Value (MEV). By hiding transaction intent (e.g., trade size and direction) until settlement, they prevent front-running and sandwich attacks. Implementations achieve this through:

• Commit-Reveal schemes where intent is submitted cryptographically before execution.
• Batch processing in zk-rollups, which obscures individual order flow.
• Encrypted mempools that prevent bots from seeing pending transactions.

In traditional finance, a dark pool is a private exchange for institutional trading. Private AMMs are the decentralized, cryptographic analog. Key comparisons include:

• Anonymity: Both hide participant identity and order size.
• Price Impact: Both aim to minimize slippage on large orders.
• Regulation: TradFi dark pools are permissioned; private AMMs are permissionless.
• Settlement: Dark pools use centralized clearing; private AMMs use smart contracts.

Private AMMs create isolated liquidity pools, fragmenting capital across multiple venues and reducing overall market depth. This leads to:

• Higher slippage for large trades due to thinner order books.
• Inefficient price discovery as liquidity is not aggregated.
• Increased arbitrage opportunities between public and private pools, which can be exploited by sophisticated actors, potentially harming regular users.

Implementing privacy requires complex cryptographic protocols like zero-knowledge proofs (ZKPs) or secure multi-party computation (MPC). This introduces:

• High computational overhead, increasing transaction latency and gas costs.
• Substantial development complexity, raising the risk of bugs and vulnerabilities.
• User experience friction, as participants may need to manage additional keys or perform complex setup procedures.

The inherent opacity of private AMMs conflicts with global financial regulations like Anti-Money Laundering (AML) and Know Your Customer (KYC) requirements. This creates:

• Legal risk for developers and liquidity providers operating in regulated jurisdictions.
• Limited institutional adoption, as regulated entities cannot participate without audit trails.
• Potential for deplatforming by infrastructure providers (e.g., RPC nodes, oracles) under regulatory pressure.

Many private AMM designs introduce new trust assumptions, moving away from the permissionless ideal of DeFi. Common issues include:

• Reliance on a trusted setup for cryptographic parameters, creating a single point of failure.
• Use of centralized sequencers or operators to batch and process transactions, which can censor or front-run.
• Dependence on a small set of actors to maintain the privacy-enhancing infrastructure.

Privacy-preserving transactions are often incompatible with the open, interoperable nature of DeFi. This results in:

• Inability to integrate with key DeFi primitives like lending protocols, yield aggregators, or cross-chain bridges that require transparent state.
• Fragmented user experience, as assets must exit the private system to interact with other applications.
• Reduced utility of liquidity, as it cannot be efficiently re-used across the broader ecosystem.

The economic model for private AMMs is unproven and faces significant hurdles:

• High operational costs for privacy computation must be offset by fees, making them less competitive.
• Chicken-and-egg problem: Liquidity providers are reluctant to join without users, and users won't come without liquidity.
• Value extraction risk: The high cost structure may necessitate centralized control over fee capture, undermining the decentralized ethos.