An Analytical Analysis of Hashing Functions in Blockchain Networks for Data Integrity Verification
We mentioned, hashing functions are important for keeping data integrity on block chain networks. The main goal of this study is to provide an analytical analysis of the different types of hashing functions used in block chain networks (for data integrity verification). With the rise of block chain, there is a clamor for more secure and effective data storage. This study explains block chain essentials and its major modules. It then explores hashing functions and their importance in block chain networks. Cryptographic properties and use case performance are compared across several types of hashing functions (SHA-256, Keccak, and BLAKE2). The conclusion is that all hashing functions offer good data integrity but are not equally fit for different use cases: SHA-256 for Bitcoin and BLAKE2s when storing file-based data. Moreover, the computational efficiency of hashing functions is also important because block chain networks can sometimes be very busy. This study's results will help choose adequate hashing functions for various block chain scenarios, making data storage and validation more efficient and safe. This work will further add to the repository of information about block chain technology and shed light on developments in hashing functions related to data reliability.
Authors
- Aneesh Wunnava (ORCID: https://orcid.org/0000-0002-3869-1324)
- Pooja Sharma (ORCID: https://orcid.org/0000-0003-0702-522X)
- D. Nancy Kirupanithi
- SB Das (ORCID: https://orcid.org/0009-0006-6195-7238)
- K. R. Nanthagobal
- Padmapriya
Institutions
- Jain University (IN)
- Siksha O Anusandhan University (IN)
- Sathyabama Institute of Science and Technology (IN)
- Chitkara University (IN)
- Jaipur National University (IN)
Publication Details
- Published
- 2025-05-29
- DOI
- https://doi.org/10.1109/netcrypt65877.2025.11102394
- Citations
- 1
- Primary Topic
- Cloud Data Security Solutions
- Type
- article
- Field-Weighted Citation Impact
- 3.25