Disadvantages of Merkle-Damgard Scheme in Cryptography
- Length extension attacks: One of the primary weaknesses of the Merkle-Damgard scheme is its susceptibility to length extension attacks. An attacker can use the hash value of a message and the length of the message to generate a new message with the same hash value, which can compromise the security of the system.
- Limited security: While the Merkle-Damgard scheme is considered to be a strong construction, it has some limitations in terms of security. For example, it is vulnerable to collision attacks, where an attacker can find two different input messages that produce the same hash value.
- Inefficient padding: The Merkle-Damgard scheme requires padding of the input message to ensure that it is a multiple of the block size. This padding can be inefficient and may result in a significant increase in the size of the message.
- Sequential processing: The Merkle-Damgard scheme processes the input message in a sequential manner, which can limit its parallelism and make it vulnerable to side-channel attacks.
- Complexity of compression function: The compression function used in the Merkle-Damgard scheme can be complex and difficult to analyze. This can make it challenging to ensure that the function is secure and free from vulnerabilities.
Merkle-Damgard Scheme in Cryptography
Pre-requisites: Cryptography and its Types
MD scheme(discovered by Ralph Merkle) is used to build collision-resistant cryptographic hash functions from collision-resistant one-way compression functions. It is used in algorithms like SHA-1, SHA-256, etc.
This scheme can be divided into two stages:
Stage 1: Design a fixed-length, collision-resistant compression function.
Stage 2: Design a CRHF H for arbitrary length messages, using ‘h’.
1. Encode the input M(with length L) for HMD to make the encoded message, a multiple of l bits. If L is already a multiple of l bits, then add an additional dummy block.
Original Message || Padding length
2. The message is then considered as t-blocks each of n bits, i.e: M1, M2…….Mt. Apply function h iteratively over the blocks of M and the previous outcome of h(i.e H1, H2…….HMD)
F(Hi-1, Mi) = Hi
3. Before starting iteration, an initial vector(H0) is used.
4. The digest HMD created after tth iteration is the compressed hash value of the original message.
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