Vitalik Buterin Unveils Plan to Restructure Ethereum Storage with Keyed Nonces

Highlights:

• Vitalik has noted that keyed nonces could help Ethereum process many transactions simultaneously.
• Privacy transactions create permanent records that increase storage needs.
• Ethereum storage may use new systems to reduce data load and improve scaling.

Vitalik Buterin, Ethereum co-founder, has suggested in a detailed post a new approach to managing Ethereum’s growing data load. The proposal introduces keyed nonces as a transaction structure that separates how Ethereum tracks transactions across privacy, standard transfers, and other use cases.

VITALIK BUTERIN TARGETS 500 BILLION PRIVACY RECORDS WITH NEW DESIGN

Ethereum $ETH co-founder, Vitalik Buterin (@VitalikButerin) has a new idea for scaling the network.

He wants to use something called keyed nonces, which could help with both privacy and storage at the same… pic.twitter.com/D4hvBAJx1T

— BSCN (@BSCNews) May 6, 2026

Ethereum currently uses a single transaction counter per user to prevent replay attacks. The single nonce method enforces strict sequencing but limits flexibility for privacy systems and blocks parallel execution. Keyed nonces introduce a system that uses a unique key with a sequence number for each transaction group.

The keyed nonce design creates separate domains by assigning different keys to transaction types such as private transfers and standard activity. Ethereum can process multiple transactions at the same time because each transaction uses an independent tracking key.

Moreover, the design supports privacy protocols that rely on nullifiers, which prevent double-spending in private transactions. Developers can assign keys derived from nullifiers to each privacy transaction to ensure proper validation.

Each transaction has its own key built upon nullifiers, so multiple transactions could run simultaneously without interfering with each other. The network improves efficiency by enabling parallel execution while maintaining strict validation rules.

Privacy Transaction Growth Could Add 500 Billion Permanent Records

Nullifiers play a central role in privacy transactions on Ethereum. Nullifiers ensure that users cannot spend the same private asset twice. Each privacy transaction creates a nullifier that remains permanently stored on-chain. Ethereum does not allow these records to be removed after creation, resulting in continuous data growth.

Buterin gave a clear example to explain the magnitude of this growth. Ethereum could process 2,000 privacy transactions per second for 8 years under high-usage conditions. That would result in approximately 500 billion nullifiers being stored on the network. All transactions will create a permanent record that steadily increases the number of nullifiers.

Ethereum’s state size could expand to extreme levels as nullifiers accumulate. Storage requirements could reach tens or even hundreds of terabytes over time. Nodes must store and verify all nullifier data to remain active participants. High Ethereum storage demands place pressure on node operators and reduce accessibility.

Builders may also face strict hardware requirements under this structure. Estimates show that builders may need about 16 terabytes of data to operate effectively. High requirements could limit participation to operators with large storage capacity. Reduced participation could weaken decentralization across the network.

In a separate earlier comment, Buterin said Ethereum must rethink its approach to network scaling. He noted that earlier reliance on layer-2 execution models no longer fits the network’s current technical state.

There have recently been some discussions on the ongoing role of L2s in the Ethereum ecosystem, especially in the face of two facts:

* L2s' progress to stage 2 (and, secondarily, on interop) has been far slower and more difficult than originally expected
* L1 itself is scaling,…

— vitalik.eth (@VitalikButerin) February 3, 2026

Ethereum Storage Models Could Support Scalable Growth

Buterin proposed restricted Ethereum storage systems to handle specific data types more efficiently. Restricted storage systems would separate high-volume records from Ethereum’s main state. Ethereum could store nullifiers in a dedicated storage layer designed for validation tasks. Dedicated storage would reduce pressure on the main state and improve data management.

Nullifiers follow simple validation rules and only confirm whether a transaction remains unspent. Simple validation allows the network to manage nullifiers without complex interactions with other data. Controlled storage structures improve efficiency when handling large volumes of predictable data.

Sharding offers one method to reduce storage pressure across nodes. Each node can store a small portion of nullifier data while remaining connected to other nodes. Distributed storage reduces the burden on individual machines. Another way to make smaller representations from verification data is to use Bloom filters. Compression saves memory by storing each nullifier more efficiently without compromising verification accuracy.