Blockchain Technology and Its Components

 


Blockchain Components

Blockchain technology, as previously stated, is characterized by its blocks which are formed into chains, hence the name blockchain. However, blockchain technology is much more complicated than just a collection of blocks and chains. It requires many other components to actually build the block and make sure that they will not be tampered with and will remain safe. Some of these important technologies include cryptographic hash functions, asymmetric-key cryptography, and ledgers [1].
The first main component of blockchain technology is the cryptographic hash functions. This is applied to data in a method called hashing. Hashing is a method used to calculate a unique output for an input of any size. The data is encrypted into a secure format which is unreadable unless the recipient has the keys. This allows individuals to take input data and hash the data to derive the same results. This proves that there has been no change to the data [1]. One of the most widely implemented hash functions in blockchain technology would be the Secure Hash Algorithm with an output of 256 bits, otherwise, known as SHA-256. The SHA256 algorithm takes inputs that have a length of less than 264 bits and releases an output that has a length of 256 bits. It has a block size of 512 bits which are represented by sixteen 32-bit words. This block of 512 enters a message compression function in 32-bit words through a message scheduler. The message scheduler then expands the 512-bit message block into sixty-four 32-bit words. The SHA256 hashing algorithms are then performed on words that are 32 bits in length, using eight working variables that are also 32 bits in length. The values of the working variable are computed at every round and this is continued until 64 rounds have been completed [2].
SHA256 also takes a 256-bit initialization vector which is fixed for the first message block. The intermediate message digest obtained at the end of the first 64 rounds is used as the initialization vector for the next message block. The SHA256 hash function is built using the Davies–Meyer construction where the initialization vector is added to the output of 64 rounds. After 64 rounds of message compression and the addition of the initialization vector, the algorithm produces an intermediate message digest of 256 bits. After the whole message block has been hashed, a value of 256 bits is obtained that is the final message digest of the input message. The SHA256 hashing algorithm is thus similar to a block cipher with a 256-bit message block size and a 512-bit key that is expanded into sixty-four 32-bit round keys using the message scheduler for each of the 64 rounds of this cipher [2].
A second important component of blockchain is asymmetric-key cryptography also known as public-key cryptography [3]. Asymmetric-key cryptography uses a pair of keys: one public and one private. The main purpose of this component is to be used in transactions such as those done in cryptocurrency. The public key is used to secure operations of the blockchain and give everyone access to the knowledge stored in the block such as the address of a single cryptocurrency in the entire network. The private key is much more restrictive and is used by an individual to digitally sign transactions.
The third important component of blockchain would be the ledger. A ledger would simply be a collection of transactions. These ledgers traditionally were centralized and operated by a single party. However, the distributed ledger is much more common in the case of blockchain [1]. Distributed ledgers are digital ledgers that are distributed across a network to all the nodes, which results in all the nodes having the same copy of the ledger. This ledger will update all nodes or holders on the network simultaneously. Distributed ledgers also authenticate information through cryptographic signature [4]. In the case of blockchain, distributed ledgers are made of blocks and all these blocks form a chain to create the entire ledger. 
Get Connected Here: =============

Website Link: https://databasescientist.org/
Nomination Link: https://databasescientist.org/award-nomination/?ecategory=Awards&rcategory=Awardee
Contact Us For Enquiry: contact@databasescientist.org

Social media=======

Youtube: https://www.youtube.com/@databasescientist
Instagram: https://www.instagram.com/databasescientist123/
Pinterest: https://in.pinterest.com/databasescientist/
Blogger: https://www.blogger.com/blog/posts/1267729159104340550

#DatabaseScience #DataManagement #DatabaseExpert #DataProfessional #DatabaseDesign #DataArchitecture #DatabaseDevelopment #DataSpecialist #DatabaseAdministration #DataEngineer #DatabaseProfessional #DataAnalyst #DatabaseArchitect #DataScientist #DatabaseSecurity #DataStorage #DatabaseSolutions #DataManagementSolutions #DatabaseInnovation #DataExpertise

Comments

Post a Comment

Popular posts from this blog

Memory Management in Flutter: Best Practices and Pitfalls

Image
  Flutter, with its promise of “write once, run anywhere,” has become the go-to framework for many developers. As with any platform, efficient memory management in Flutter is crucial to ensure fast performance, responsiveness, and a seamless user experience. While Dart, the language behind Flutter, comes with built-in garbage collection, a thorough understanding of memory management can help avoid common pitfalls and enhance app performance.  Let’s delve into the best practices and potential traps in Flutter memory management. Why is Memory Management Important?Performance: Efficient memory usage ensures faster app operation and smooth animations. Resource Conservation: Mobile devices have limited memory. Optimizing memory use ensures that other apps on the device run smoothly. User Experience: An app that efficiently manages memory reduces the chances of crashes and enhances user satisfaction. Best Practices:Leverage Dart’s Garbage Collection: Dart automatically deallocates o...
Read more

Metadata

Image
  information that describes the characteristics of a dataset, information resource, or object. This descriptive information provides context and helps users find, organize, manage, and understand the core data. Examples of metadata Metadata is common in many areas, both digital and physical:Computer files: A file's metadata includes its name, file type, size, creation date, and last modified date. Photographs: A digital image file contains metadata from the camera's Exchangeable Image File Format (EXIF). This includes information like the date and time the photo was taken, the device used, and sometimes even GPS location data. Books: The metadata for a book includes its title, author, publisher, ISBN, and table of contents. Web pages: Search engines and browsers use metadata found in HTML meta tags, such as a page's title, description, and keywords, to understand and display its content. Video and audio files: These files can contain metadata describing their codec, video ...
Read more

Large Language Models and Vector Databases for News Recommendations

Image
  Large Language Models   (LLMs) generated a global buzz in the machine learning community with recent releases of generative AI tools such as Chat-GPT, Bard, and others alike. One of the core ideas behind these solutions is to compute a numerical representation of unstructured data (such as texts and images) and find similarities between these representations. However, taking all of these concepts into a production environment has its own set of machine learning engineering challenges: How to generate these representations quickly? How to store them in a proper database? How to quickly compute similarities for production environments? In this article, I introduce two open-source solutions that aim to solve these questions: Sentence Transformers  [1] : an embedding generation technique based on textual information and; Qdrant : a vector database capable of storing embeddings and providing an easy interface to query them. These tools are applied to NPR [2], a News Portal R...
Read more