Bloom Filter Variations for Enhanced Log Search in Ethereum

Efficient data retrieval is a critical challenge for blockchain platforms as they scale. Ethereum leverages Bloom Filters to accelerate log retrieval by reducing computational overhead. However, the default Bloom Filter implementation introduces false positives and inefficiencies in handling the rapidly growing data size of the Ethereum blockchain. This paper explores Ethereum-specific variations of Bloom Filters to mitigate these limitations. We propose three optimized methods that take advantage of the unique characteristics of Ethereum logs and block structures, designed to minimize false positive rates, improve retrieval speed, and optimize storage requirements, with trade-offs between computational overhead and retrieval accuracy. Our evaluations demonstrate that these variations outperform standard Bloom Filters in Ethereum, contributing to the advancement of blockchain data structures.