In cryptography and computer science, a hash tree or Merkle tree is a tree in which every "leaf" (node) is labelled with the cryptographic hash of a data block, and every node that is not a leaf (called a branch, inner node, or inode) is labelled with the cryptographic hash of the labels of its child nodes. A hash tree allows efficient and secure verification of the contents of a large data structure. A hash tree is a generalization of a hash list and a hash chain.
Demonstrating that a leaf node is a part of a given binary hash tree requires computing a number of hashes proportional to the logarithm of the number of leaf nodes in the tree. Conversely, in a hash list, the number is proportional to the number of leaf nodes itself. A Merkle tree is therefore an efficient example of a cryptographic commitment scheme, in which the root of the tree is seen as a commitment and leaf nodes may be revealed and proven to be part of the original commitment.
The concept of a hash tree is named after Ralph Merkle, who patented it in 1979.
Hash trees can be used to verify any kind of data stored, handled and transferred in and between computers. They can help ensure that data blocks received from other peers in a peer-to-peer network are received undamaged and unaltered, and even to check that the other peers do not lie and send fake blocks.
Hash trees are used in hash-based cryptography. Hash trees are also used in the (IPFS), Btrfs and ZFS file systems (to counter data degradation); Dat protocol; Apache Wave protocol; Git and Mercurial distributed revision control systems; the Tahoe-LAFS backup system; Zeronet; the Bitcoin and Ethereum peer-to-peer networks; the Certificate Transparency framework; the Nix package manager and descendants like GNU Guix; and a number of NoSQL systems such as Apache Cassandra, Riak, and Dynamo.
Suggestions have been made to use hash trees in trusted computing systems.
The initial Bitcoin implementation of Merkle trees by Satoshi Nakamoto applies the compression step of the hash function to an excessive degree, which is mitigated by using Fast Merkle Trees.
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