Discrepancy Detection in Merkle Tree-Based Hash Aggregation

IEEE International Conference on Blockchain and Cryptocurrency (ICBC), pp. 1–9

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.

Authors 3

1. Thomas Osterland Aachen Fraunhofer FIT

   RWTH Aachen University · Fraunhofer Institute for Applied Information Technology

   Affiliation as printed

   RWTH Aachen University,Fraunhofer FIT,Sankt Augustin,Germany

   Fraunhofer FIT, RWTH Aachen University, Sankt Augustin, Germany

2. Gordon Lemme

   Fraunhofer Institute for Machine Tools and Forming Technology

   Affiliation as printed

   Fraunhofer IWU,Chemnitz,Germany

   Fraunhofer IWU, Chemnitz, Germany

3. Thomas Rose Aachen Fraunhofer FIT

   RWTH Aachen University · Fraunhofer Institute for Applied Information Technology

   Affiliation as printed

   RWTH Aachen University,Fraunhofer FIT,Sankt Augustin,Germany

   Fraunhofer FIT, RWTH Aachen University, Sankt Augustin, Germany