Discrepancy Detection in Merkle Tree-Based Hash Aggregation

Abstract

Hash aggregation is an accepted approach to mitigate the burden of storing substantial amounts of data on a distributed ledger. Merkle trees are used to derive a single hash from data and ensure the integrity of the aggregated individual information. However, to identify a single manipulated datum or a subset of manipulated data, one needs to have access to the entire Merkle tree. This is not a problem if the Merkle tree is stored on the distributed ledger. However, for substantial amounts of hashes, such a tree can become quite large. At some point it is not longer feasible to store the tree on the ledger. Especially, when aggregating large numbers of transactions that occur in a high frequency. In this paper, we discuss four approaches to identify manipulated data in a Merkle tree without the need to persist the entire Merkle tree on the distributed ledger.