Amount Awarded: $18,000
Blockchains are often touted as immutable ledgers, but querying their historical state remains costly, inefficient, and frequently centralized. As ledgers grow over time, storing, serving, and verifying past data—such as the state of an account or inclusion of a transaction in a block—requires either recomputation or reliance on third-party services. This creates bottlenecks for blockchain analytics, compliance auditing, crime investigation, and general accountability, all of which depend on fast, verifiable access to historical data. While decentralization is a core blockchain principle, today’s historical query infrastructure often reintroduces central points of failure, undermining trust in the ecosystem.
We propose Rubix, a storage and proof framework designed to support efficient historical queries while keeping storage and computation overheads low. Our approach is broadly applicable to any blockchain with authenticated data structures, such as Merkle or Verkle trees, and consists of three core techniques: (1) Merkle Forests, which reduce proof generation overhead by splitting large trees into smaller ones linked via KZG commitments; (2) Bivariate Commitments, which enable efficient batch queries across blocks through polynomial-based aggregation; and (3) Rubix Cube Sharding, a decentralized data layout that mirrors the structure of the commitments, enabling load-balanced, fault-tolerant, and locality-aware querying.
These techniques preserve cryptographic verifiability, minimize on-the-fly computation, and avoid full duplication of ledger history. They also support both point and batch queries, enabling practical applications in auditing, compliance, and forensics. To validate our approach, we implement prototypes and evaluate them using real-world workloads and Ethereum client software. While our methods apply to a broad class of blockchains, we focus this project on Ethereum, where the challenges of historical querying are especially acute and have direct practical relevance.