Cryptographic Hash Function Reflection Definition of Cryptographic Hash Functions
Cryptographic Hash Function (CHF) is an equation or algorithm that maps a particular data set into a hash value (Wang et al., 2017). It can simply be defined as a mathematical function that derives a special string of texts from any form of arbitrary data. The hash value, in this case, refers to the produced enciphered text and is mostly of fixed size. Such converted plaintexts are usually fitted into complex data structures or tables that literary holds fixed-size elements. The generated structures/tables are termed the hash tables and are deemed to be secure. CHF tends to be mistaken for encryption, but the main difference is that it is a one-way function, unlike the latter, which can be deciphered using a unique key. There are several hash functions, including secure hashing algorithm 1 and secure hashing algorithm 256.
Purpose of CHF in Database Design
CHF is widely used in database design for three primary reasons: authentication, data verification, and storage of sensitive information (Saez et al., 2019). These distinctive features and capabilities play a critical role in the maintenance of a secure and reliable database. Authenticity ensures that the input data will not be corrupted in any way and can only be accessed by rightful persons. CHF is also entrusted with data verification since every plaintext is assigned a different output format. Therefore, data cannot be mistaken, and the hash value can only be used to verify the user. CHF is further reliable in storing sensitive data since one can hardly identify the input used to generate a secure cryptographic hash value by just looking at it. As already stated, CHF-enciphered text cannot be reversed to gain the specific details used in its creation. Therefore, this method turns out to be the best fit for database design.
Proposal
Business Applications for CHF
CHF is widely used in business as a secure means of protecting various credentials in the long run. It is applied in password storage to ensure that such details do not fall into intruders’ hands. The hashing capability ensures that passwords are not stored in plaintext, which can easily be accessed. It creates a digest/hash value that is consequently stored in the hash table. Therefore, an attacker can only view the hash values and do nothing about them. CHF is also used in password verification in that for a user to be granted access to the system, their input must match the hash value of actual users on the server side (Saez et al., 2019). In addition, a business is always assured that data integrity is optimized to the best level possible. The hashing function assures the user that the stored data is not compromised and its originality is maintained. Therefore, it can be deduced that CHF’s ability to store and verify data while maintaining its integrity contributes to its increased use in business.
Relationship between Hash Function and Security in Database Design
A hash function has several properties that reinforce security in database design. First, its aspect of determinism ascertains that a particular credential outputs the same hash value to avoid override. At the same time, the hashing function is nonpredictable in that one can hardly determine the enciphered text from the plaintext. Another reason is the collision resistance property, which implies that it is relatively impossible to detect different passwords that are mapped to the same hash function. Finally, the hash function has the avalanche effect, whereby a slight change in the password results in an unpredictable and significant change in the hash value. These features demonstrate that hash functions are prioritized in database design (Wang et al., 2017).
References
Saez, Y., Estebanez, C., Quintana, D., & Isasi, P. (2019). Evolutionary hash functions for specific domains. Applied Soft Computing, 78, 58-69.
Wang, D., Jiang, Y., Song, H., He, F., Gu, M., & Sun, J. (2017). Verification of implementations of cryptographic hash functions. IEEE Access, 5, 7816-7825.