Hashing and Loss of Information, Key Derivation

Hash functions lose  information that is present in the input data. This is required in order to generate a fixed-length output hash value.

This loss of information makes it IMPOSSIBLE to recover the original input data from the output hash value.

In addition, many hash functions use key derivation functions to generate the hash value, which further complicates the process of recovering the original input data.

The Avalanche Effect in hashing:

  • A good hash function exhibits the avalanche effect. A  small change in the input data should result in a significant change in the hash value.
  • This makes it difficult for an attacker to guess the input data by modifying the hash value.
  • In other words, even a small change in the input data should cause a completely different hash value to be produced, which further obscures the relationship between the input data and the hash value.

Salt:

In some cases, a salt is added to the input data before hashing.

  • The Salt is a random value that is added to the input data.
  • This makes it more difficult for an attacker to guess the input data by pre-computing a table of hash values for common input data.
  • When a salt is used, the attacker would need to pre-compute a table of hash values for every possible salt value, which significantly increases the computational effort required to guess the input data.

Anuj holds professional certifications in Google Cloud, AWS as well as certifications in Docker and App Performance Tools such as New Relic. He specializes in Cloud Security, Data Encryption and Container Technologies.

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Anuj Varma – who has written posts on Anuj Varma, Hands-On Technology Architect, Clean Air Activist.

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