How to Architect a Censorship-Resistant Transaction Flow

A censorship-resistant transaction flow ensures that a user's ability to submit a transaction to a network cannot be blocked by any single entity, including validators, miners, or network intermediaries. This is a foundational property for applications where permissionless access is critical, such as decentralized finance (DeFi), voting systems, and public goods funding. Architecting for this property requires moving beyond reliance on a single RPC endpoint or centralized sequencer, as these present single points of failure that can be exploited to filter or block transactions.

The primary strategy involves creating redundant pathways for transaction submission. Instead of sending a transaction directly to an Ethereum node via Infura or Alchemy, your application should integrate with multiple RPC providers from different companies and geographical regions. Furthermore, you can leverage a transaction relayer network like the Flashbots Protect RPC or a mev-geth-compatible endpoint, which broadcasts transactions directly to block builders, bypassing the public mempool and reducing the risk of front-running and censorship by traditional validators.

For maximum resilience, incorporate direct peer-to-peer (P2P) submission methods. On Ethereum, you can run your own Ethereum node (e.g., using Geth or Nethermind) and use it as a primary broadcast mechanism. For other chains, investigate if they support libp2p or similar P2P gossip protocols for transaction propagation. Your architecture should implement a fallback hierarchy: first attempt submission via your private node, then through a relayer network, and finally through a set of public RPCs, ensuring the transaction finds a path to the network.

Smart contract design also plays a role. Protocols can be built to accept signed messages (e.g., EIP-712) that can be submitted by any third party, separating the signer from the submitter. This allows users to generate a valid transaction intent offline, which can then be broadcast by a network of permissionless relayers or even other users, making it virtually impossible to censor the original user. This pattern is used in gasless meta-transaction systems and some voting applications.

Finally, monitoring is essential. Your system should track transaction propagation metrics—such as time-to-first-seen on different nodes and inclusion latency—to detect if certain paths are being filtered. Tools like Ethereum execution client diversity dashboards and block explorer APIs can help audit the health of your submission network. By combining redundant infrastructure, P2P tools, smart contract patterns, and active monitoring, you can architect a robust, censorship-resistant transaction flow for your application.

A censorship-resistant transaction flow ensures that a user's ability to submit a transaction to a network cannot be blocked by a centralized entity, such as a validator, a sequencer, or a government. This is a fundamental property of decentralized systems like Bitcoin and Ethereum, but it can be compromised by network-level censorship, MEV (Maximal Extractable Value) attacks like time-bandit attacks, or compliant infrastructure providers. Architecting for resistance requires a multi-layered approach that considers the transaction's entire lifecycle: creation, propagation, and inclusion.

The foundation lies in understanding the network layer. You must design your client to connect to a diverse, permissionless set of peers. Relying on a single RPC provider like Infura or Alchemy creates a central point of failure. Instead, implement logic to connect directly to the peer-to-peer (P2P) network or use a decentralized RPC network like BlastAPI or Lava Network. For Ethereum, this means running or connecting to an execution client (e.g., Geth, Nethermind) and a consensus client (e.g., Lighthouse, Prysm). Tools like Ethereum Node Service (ENS) can help manage these connections.

Next, you must address transaction propagation. A transaction sent to a single, potentially censoring node can be ignored. To mitigate this, use a technique called transaction gossiping or broadcasting to multiple, randomly selected peers. Libraries like Ethers.js broadcast or Viem's sendRawTransaction can be wrapped to send the signed transaction payload to several RPC endpoints concurrently. More advanced solutions involve using a flashbots-style relay (like the Flashbots Protect RPC) or a suave-enabled builder to route transactions directly to block builders who are economically incentivized to include them, bypassing the public mempool.

Finally, consider inclusion guarantees. On networks with proposer-builder separation (PBS), like post-Merge Ethereum, the entity that builds a block (the builder) is separate from the entity that proposes it (the proposer/validator). You can increase censorship resistance by ensuring your transaction is included in a block body, not just the public mempool. This is where encrypted mempools (e.g., using Shutter Network) or commit-reveal schemes come into play. They hide transaction content from builders until the block is proposed, preventing them from censoring based on the transaction's data or destination address.

Censorship resistance in blockchain is the property that prevents any single entity from blocking a valid transaction. The primary attack vectors are at the network layer (e.g., IP-level blocking by ISPs), the mempool layer (where validators ignore specific transactions), and the consensus layer (where validators collude to exclude blocks). A robust architecture must defend against all three. For example, a validator running on a centralized cloud provider like AWS could be pressured to censor transactions, making infrastructure decentralization a first-line defense.

To counter network-layer censorship, implement transaction propagation redundancy. Instead of broadcasting a transaction to a single public node, use a multi-RPC provider strategy. Services like Chainlist provide lists of public RPC endpoints. In your application, you can programmatically send the transaction to multiple nodes. For instance, using ethers.js: await Promise.any([provider1.sendTransaction(tx), provider2.sendTransaction(tx)]). This ensures the transaction enters the network even if one gateway is filtering traffic.

Mempool-level censorship, often called miner extractable value (MEV) abuse, is addressed by using private transaction relays. These services, such as Flashbots Protect or Titan, submit transactions directly to validator builders through encrypted channels, bypassing the public mempool. This hides the transaction from bots that might front-run or censor it. Architecturally, integrate a relay's RPC URL as a priority endpoint in your wallet or dApp backend, ensuring sensitive transactions use this shielded path.

For consensus-layer resistance, the ultimate defense is credibly neutral fork choice. This means the network protocol must have a clear rule for choosing the canonical chain that is independent of transaction content. Ethereum's move to Proof-of-Stake with LMD-GHOST and proposer-builder separation (PBS) are examples. As an application developer, you can support this by encouraging users to run their own nodes or use decentralized RPC networks like POKT or Ankr, which aggregate many independent node operators.

A complete censorship-resistant flow combines these layers. A user's transaction should be signed locally, submitted via a private relay to avoid MEV, and the application should monitor inclusion using a fallback of decentralized RPCs. Furthermore, integrating with cross-chain or layer-2 alternatives provides an escape hatch; if a transaction is censored on one chain, the user can bridge assets and execute it on another. This multi-layered, redundant approach makes systematic censorship economically and practically infeasible.

A censorship-resistant transaction flow begins by architecting a robust, decentralized mempool propagation layer. The goal is to ensure a transaction submitted by any user has a high probability of reaching an honest block producer, even if certain network nodes or validators attempt to filter it. This is achieved by moving away from reliance on a single RPC endpoint or a small set of centralized broadcast nodes. Instead, the system uses a gossip protocol where nodes relay transactions to multiple peers, creating a redundant mesh network. Key metrics for this layer include propagation latency (time to reach most nodes) and network coverage (percentage of nodes that see the transaction).

The core implementation involves running a lightweight client that connects to the peer-to-peer (P2P) network of a blockchain. For Ethereum, this means connecting to the devp2p or Discv5 discovery protocol. The client must maintain connections to a diverse set of peers across different geographic regions and network providers to avoid a single point of failure. Upon receiving a new transaction, the client should immediately gossip it to all connected peers, who will then do the same. Libraries like Libp2p provide a modular framework for building such networks, handling peer discovery, connection multiplexing, and protocol negotiation.

To counter validator-level censorship (e.g., a dominant block producer ignoring certain transactions), propagation must prioritize reaching a broad and unbiased subset of the network. Strategies include peer diversity scoring, which penalizes nodes that consistently relay filtered transaction sets, and sentry node architectures, where validators are shielded behind a layer of neutral relay nodes. The Flashbots SUAVE initiative explores a separate mempool for MEV transactions to reduce the incentive for validators to censor the public mempool. Implementing transaction rebroadcasting logic is also crucial, where a node re-sends a transaction if it hasn't been included in a block after a timeout.

Here is a simplified Python pseudocode example using asyncio and a hypothetical P2P library to demonstrate the broadcast loop:

pythonCopy
import asyncio
from p2p_network import NetworkClient

class CensorshipResistantBroadcaster:
    def __init__(self, bootnodes):
        self.network = NetworkClient(bootnodes)
        self.seen_txs = set()

    async def broadcast_transaction(self, raw_tx):
        tx_hash = self._calculate_hash(raw_tx)
        if tx_hash in self.seen_txs:
            return
        self.seen_txs.add(tx_hash)
        # Gossip to all connected peers
        for peer in self.network.get_peers():
            await peer.send("tx", raw_tx)
        print(f"Broadcast tx {tx_hash[:16]} to {len(self.network.get_peers())} peers")

    async def handle_incoming_tx(self, raw_tx, peer):
        tx_hash = self._calculate_hash(raw_tx)
        if tx_hash not in self.seen_txs:
            self.seen_txs.add(tx_hash)
            # Re-gossip to other peers (flood routing)
            for other_peer in self.network.get_peers():
                if other_peer != peer:
                    await other_peer.send("tx", raw_tx)

Monitoring and incentivization are critical for network health. Operators should track peer count, transaction acceptance rates, and propagation hop counts. Projects like BloxRoute and Blockdaemon operate specialized relay networks that guarantee fast, reliable broadcast. For true decentralization, however, the protocol should incentivize individual users and smaller node operators to participate in gossip. This could be done through protocol-level rewards for relaying or by integrating with systems like Ethereum's Portal Network, which aims to provide lightweight access to chain data. The endpoint for a user-facing application should abstract this complexity, providing a simple sendRawTransaction API that internally manages the P2P broadcast layer.

In practice, integrating this requires balancing latency, reliability, and resource usage. A hybrid approach is often best: initially broadcasting via a trusted public RPC (e.g., Infura, Alchemy) for speed, while simultaneously initiating a P2P broadcast to ensure censorship resistance. Developers should consult the specific P2P specification for their target chain (e.g., Ethereum's EIP-2464 for eth/66 protocol) and use established libraries. The final architecture ensures that no single entity can prevent a valid transaction from being considered for inclusion, which is a foundational property for credible neutrality and permissionless access in decentralized networks.

A multi-relay submission strategy is a critical architectural pattern for applications requiring high censorship resistance. The core principle is simple: never rely on a single transaction relay or RPC provider. Instead, your application should be configured to submit transactions through multiple, independent pathways. This design mitigates the risk of a single point of failure, whether due to malicious censorship, technical downtime, or regional blocking. In practice, this means integrating with several services like Flashbots Protect, BloXroute, or a private mev-geth instance, alongside standard public RPC endpoints from providers like Alchemy or Infura.

Architecturally, this requires an orchestration layer within your application's backend. This component is responsible for managing a list of relay endpoints, handling submission logic, and monitoring for failures. A robust implementation doesn't just broadcast to all relays simultaneously, as this can lead to transaction duplication and wasted gas. Instead, it employs a primary-fallback or race strategy. You might first attempt submission through a prioritized relay (e.g., Flashbots for MEV protection), and only upon a timeout or explicit error, fail over to the next relay in the sequence.

For developers, implementing this starts with configuring multiple RPC URLs in your environment. Using Ethers.js v6 or Viem, you instantiate separate provider or client objects for each endpoint. Your submission function should wrap the sendTransaction call in error handling and retry logic. Here's a simplified conceptual pattern:

javascriptCopy
const relays = [providerFlashbots, providerBloXroute, providerPublic];

for (const provider of relays) {
  try {
    const tx = await provider.sendTransaction(signedTx);
    return tx.hash; // Success, exit loop
  } catch (error) {
    console.log(`Relay failed: ${error.message}`);
    // Continue to next relay
  }
}
throw new Error("All relay submissions failed");

This ensures your transaction has multiple avenues to reach the network.

Key considerations for production systems include monitoring and health checks. Your orchestration layer should track metrics like submission latency, success rate, and failure modes for each relay. This data can be used to dynamically reorder the priority of relays, temporarily deprecate unhealthy endpoints, and trigger alerts. Furthermore, be mindful of nonce management; your application must track the pending nonce accurately across all submission attempts to prevent conflicts. Using a centralized nonce manager or a database-locked process is often necessary.

The ultimate goal is to create a defensive submission pipeline that maximizes the probability of inclusion. This is especially vital for time-sensitive transactions in DeFi (e.g., liquidations, arbitrage) or governance actions where censorship could have significant consequences. By decentralizing the submission layer itself, you align your application's resilience with the decentralized ethos of the underlying blockchain, ensuring user transactions can bypass any single entity's gatekeeping.

Following the Office of Foreign Assets Control (OFAC) sanctions on the Tornado Cash smart contracts in August 2022, a critical vulnerability in Ethereum's design became apparent: transaction censorship. While the sanctioned contracts themselves were immutable, centralized infrastructure providers like RPC node operators and block builders (searchers) began filtering and rejecting transactions interacting with blacklisted addresses. This highlighted that censorship resistance depends not just on decentralized consensus, but on the entire transaction supply chain from user to block inclusion.

To architect a resilient flow, we must understand the points of failure. A standard transaction passes through: the user's wallet -> a node RPC endpoint (e.g., Infura, Alchemy) -> the public mempool -> a block builder -> a validator. Censorship can occur at the RPC layer (transaction rejection) or the builder/validator layer (exclusion from a block). The goal is to create a system where a single adversarial actor cannot prevent a valid transaction from being included, leveraging credible neutrality and decentralization at each stage.

The first line of defense is RPC endpoint diversity. Instead of relying on a single centralized provider, applications should integrate multiple RPC endpoints and implement client-side logic to retry failed submissions. More robust solutions involve using a decentralized RPC network like Blast API or Pocket Network, which distributes requests across many independent node operators, making coordinated censorship significantly harder. For developers, this means configuring fallback providers in libraries like Ethers.js or Viem.

Bypassing the public mempool entirely is a powerful technique. Services like Flashbots Protect or Taichi Network allow users to submit transactions directly to a network of block builders via a private channel. This not only avoids frontrunning but also obscures the transaction from censoring RPC nodes. The transaction is only revealed when a builder includes it in a block proposal. Implementing this requires using a specialized RPC URL and, often, a bundler service to package the transaction.

For maximum resilience, the transaction must reach the validator. This is where builder diversity and proposer-builder separation (PBS) become crucial. If a major builder censors, the transaction must be routable to an alternative, non-censoring builder. Projects like Eden Network and Relayoor act as anti-censorship relays, ensuring transactions are broadcast to multiple builders. In a mature PBS ecosystem, validators (proposers) can also choose builders based on their inclusion policies, creating economic pressure against censorship.

A complete, censorship-resistant system integrates these layers. A practical implementation for a dApp might: 1) Use a decentralized RPC as the primary with centralized fallbacks, 2) Route sensitive transactions through a private mempool service like Flashbots, and 3) Sponsor its own mev-boost relay that guarantees inclusion. The code example below shows a basic Ethers.js setup with a fallback provider and Flashbots RPC. The ongoing evolution of encrypted mempools and suave will further enhance these designs, moving the ecosystem toward stronger guarantees of permissionless access.

Architecting a censorship-resistant transaction flow requires a multi-layered approach. The foundation is decentralized transaction submission, which can be achieved by integrating with a network of RPC providers like Chainscore or using a direct peer-to-peer mempool. This ensures no single entity can block your transaction's broadcast. The next layer is transaction privacy, which involves using techniques like CoinJoin, stealth addresses, or zk-SNARKs to obfuscate the link between sender and receiver, making it difficult for adversaries to identify and filter specific users.

For developers, the next step is to implement these concepts. Start by replacing a single centralized RPC endpoint in your dApp's frontend or backend with a provider that offers redundant, geographically distributed nodes. Libraries like ethers.js or viem can be configured with multiple RPC URLs and fallback logic. For advanced privacy, integrate with protocols such as Tornado Cash (on Ethereum) or the Aztec network, which allow for private transactions via zero-knowledge proofs. Always audit the smart contracts of these privacy tools before integration.

Testing your architecture is critical. Simulate censorship attacks by configuring a local testnet (e.g., Hardhat, Anvil) with modified nodes that reject transactions from specific addresses. Use tools like Tenderly to fork mainnet and test your fallback submission logic under realistic conditions. Monitor key metrics: inclusion time, broadcast success rate, and provider latency. Establishing these benchmarks will help you identify bottlenecks and prove the resilience of your system.

The landscape of censorship resistance is evolving. Keep abreast of new developments like SUAVE (Single Unifying Auction for Value Expression) for decentralized block building, PBS (Proposer-Builder Separation) implementations, and encrypted mempools such as those proposed by Shutter Network. Participating in governance forums for networks you build on (e.g., Ethereum's EIP process) is also crucial, as protocol-level changes can significantly impact application-layer resistance.

Finally, document your architecture and share your findings. Publishing a detailed breakdown of your transaction flow, including the providers used, privacy mechanisms implemented, and test results, contributes to the collective knowledge of the Web3 ecosystem. This transparency builds trust with your users and helps other developers architect more robust systems. The goal is a network where applications are not just decentralized in state, but in their very ability to execute.