Abstract:

To address the shortcomings of existing provenance mechanisms in handling log data, this article proposed a blockchain-based log data provenance mechanism using Bloom filter search tree. This mechanism adopted a storage model combining on-chain data hashes and off-chain actual data was adopted to ensure data security and privacy. For the on-chain data hashes, this article introduced a bittimemap-Bloom filter tree (BT-BFT) mechanism centered around access time as the core index information. This mechanism indexed timestamps using a bitmap, then leveraged the number of Bloom filters to delineate the granularity of log data analysis, subsequently constructing a balanced tree-like structure that enabled efficient mapping and indexing of time-dimensional data. Deployed in the form of smart contracts, it effectively filtered out non-target provenance nodes, supported timestamp-based provenance and full-period provenance based on keywords, and incorporated an identity filtering mechanism to address the inherent conflict issues of Bloom filters. Experimental results demonstrate that the mechanism proposed in the article can accurately and efficiently trace time-sensitive data while ensuring data security.

Key words: blockchain, log data, data provenance, Bloom filter