In the cryptocurrency world, the Merkle Tree contains a summary of the transactions made with the hash function in blockchain networks. The Merkle Tree provides the simplest and most efficient way to verify blocks of transactions on blockchain networks.
The Merkle Tree was invented by computer scientist Ralph Merkle. Ralph Merkle obtained the patent rights to the Merkle Tree structure in 1979. Ralph Merkle later published his paper "Digital Signature Based on a Traditional Cryptographic Function" in 1987. In this paper, Ralph Merkle mentioned Merkle Trees. A Merkle Tree is a data structure in the form of a schema containing hash digests. The hash digest codes of the hash data on the Merkle Tree are sorted from bottom to top and form a schema. In the Merkle Tree, the addition of the hashes in the schema at the bottom determines the hash values at the top.
With the Merkle Tree, the integrity of the data in the entire system is better ensured by placing the hash data and hash summaries on a single schema and collecting the hash values generated from the bottom up in a sequential manner. Merkle Tree is used in peer-to-peer data transfers through computers and in the verification of all transferred data. With the use of Merkle Tree, it can be learned whether the transferred data or data blocks are damaged or forged. The authenticity of the blocks can be confirmed with Merkle Trees.
How Does Merkle Tree Work?
Merkle Trees are created by aggregating all of the hash summaries in the blocks. At the bottom of the Merkle Tree is the data of the transactions. The hash value (hash code) of the data here is generated using hash functions. Then, the values at the bottom of the schema consisting of hashes are added together. The resulting value is added to the other rows above. As a result of this process, Merkle Trees are created. When all these processes are completed, the concepts such as hash value, Merkle root, and root hash appear. The calculated root hash value indicates whether there is an error in any block. If there is a mismatch in the values that make up the root hash, the algorithm in the system continues to operate using hash functions until it generates the correct and error-free Merkle root.
Why Is Merkle Tree Used in the Bitcoin Blockchain Network?
The Merkle Tree offers a type of ease of operation for miners who want to verify blocks on the Bitcoin blockchain network. In the Bitcoin blockchain network, miners verify blocks on the network using hash functions. The data on the blockchain network is sorted and placed in the Merkle Tree. The algorithm then works with the hash functions to generate the root hash. The resulting root hash can then be used in the same way for all subsequent mining operations. In this case, instead of hashing blocks, all miners in the network only hash the root hash and continue their operations.
What Are the Advantages of Merkle Tree?
Merkle Trees are used in digital signatures, blockchain networks and many other cryptographic applications. Here are some advantages of Merkle Trees:
- Integrity Check
- Suitability for Data-Driven Operations
- Effective Verification
- Security
- Efficiency in Data Storage
Integrity Check
Merkle Trees can be used to verify the integrity of all data. A unique cryptographic digest is calculated for all parts of any file or data group. These digests are then organized hierarchically in a tree structure. Through this process, if any changes are made to the data group or data set, one or more of the digests will change. At the same time, the root summary of the tree also changes. In this case, the root summary can be checked to ensure integrity.
Suitability for Data-Driven Operations
The Merkle Tree offers a data structure that is well suited for distributed and data-centric applications. For instance, blockchain networks use Merkle Trees that are linked together using the hash of each block. Thus, only the root hash of the Merkle Tree is needed to verify all transactions in the blocks.
Effective Verification
Merkle Trees greatly reduce the computational burden of verifying any data. Since it is only necessary to verify the root hash, instead of computing hashes from all nodes in the Merkle Tree, only one or two hashes need to be computed. This provides a performance advantage, especially when used in large data pools.
Security
Because Merkle Trees are built by concatenating different cryptographic hashes, they provide protection for data integrity while also ensuring data confidentiality. In addition, Merkle Trees make it easier to detect the ordering or alteration of data.
Efficiency in Data Storage
Merkle Tree is an efficient technique for storing and accessing all data. The structure of Merkle Trees allows all data to be organized hierarchically. Data access is also greatly accelerated. All similar data becomes easier to reuse and this reduces storage costs.
What Are the Disadvantages of Merkle Tree?
Some disadvantages of Merkle trees are as follows:
- Memory Requirements
- High Processing Power Requirement
- Data Addition and Deletion Challenges
- Security Challenges
- Data Fragmentation
Memory Requirements
Since Merkle Tree keeps all the data in memory, it requires much larger memory requirements for large data deposits.
High Processing Power Requirement
Merkle Tree computations are very fast, even on large data pools. But the computational requirements are also very high. A summary is needed for each piece of all the data. Depending on the size of the data pool, there is a slowdown in speed and the need for processing power.
Data Addition and Deletion Challenges
Merkle Trees are used to track changes in data sets, but adding and deleting data can sometimes be more difficult. In particular, an existing data structure may need to be recalculated. This requires extra processing power and time.
Security Issues
Merkle Trees can be subject to security issues such as hash collisions. If a hash collision occurs, the integrity and security of the data is compromised. Therefore, it is critical that Merkle Trees are operated correctly and that the summary calculations are performed correctly.
Data Fragmentation
Merkle Tree divides the data into parts. It then creates a summary for each part. In this case, if the dataset is too small, the use of Merkle Tree may be redundant and efficiency may decrease.
