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SEARCH: Symmetric Encryption Algorithm Research

Overview

The DifferentialAnalyzer Python library is designed for cryptanalysts to determine optimal differential characteristics in various contexts. The class provides several methods for differential analysis, focusing on characteristics based on given constraints.

Key Methods

The following methods are essential for differential analysis:

find_optimal_characteristic()

This method determines the optimal differential characteristic based on standard constraints of the algorithm. It considers only permutation, substitution, and the differential distribution table (DDT).

Signature:

find_optimal_characteristic(self, min_blocks=1, max_blocks=1, prune_level=-1, repeat=1, excluded_inputs_from_pruning=None, print_paths=True)

Parameters:

  • min_blocks: Minimum number of active S-boxes in the final result.
  • max_blocks: Maximum number of active S-boxes in the final result.
  • prune_level: Pruning threshold for the DDT. If set to -1, the solver initializes with the best pruning threshold.
  • repeat: Number of solutions to find. If not specified, only one solution is found.
  • excluded_inputs_from_pruning: List of input values to exclude from pruning.
  • print_paths: Whether to print intermediate solutions. Default is True.

find_optimal_characteristic_with_fixed_delta_in()

This method finds the optimal differential characteristic with a fixed input difference.

Signature:

find_optimal_characteristic_with_fixed_delta_in(self, delta_in, min_blocks=1, max_blocks=1, prune_level=-1, repeat=1, print_paths=True)

Parameters:

  • delta_in: Desired input difference (integer).
  • min_blocks, max_blocks, prune_level, repeat, print_paths: Same as find_optimal_characteristic.

find_optimal_characteristic_with_fixed_delta_out()

This method finds the optimal differential characteristic with a fixed output difference.

Signature:

find_optimal_characteristic_with_fixed_delta_out(self, delta_out, min_blocks=1, max_blocks=1, prune_level=-1, repeat=1, print_paths=True)

Parameters:

  • delta_out: Desired output difference (integer).
  • min_blocks, max_blocks, prune_level, repeat, print_paths: Same as find_optimal_characteristic.

find_optimal_characteristic_iterative()

This method finds the optimal differential characteristic iteratively, where the input difference equals the output difference.

Signature:

find_optimal_characteristic_iterative(self, min_blocks=1, max_blocks=1, prune_level=-1, repeat=2, print_paths=True)

Parameters:

  • min_blocks, max_blocks, prune_level, repeat, print_paths: Same as find_optimal_characteristic.

find_best_differential()

This method improves an already found differential characteristic by finding alternative paths.

Signature:

find_best_differential(self, delta_in, delta_out, repeat=1, print_paths=True)

Parameters:

  • delta_in: Fixed input difference (integer).
  • delta_out: Fixed output difference (integer).
  • repeat: Number of solutions to find. Default is set to 2.
  • print_paths: Whether to print intermediate solutions. Default is True.

Usage Example

You can take as an example main.py, rectangle.py, gift.py or present.py

Ensure you have the necessary dependencies installed and properly configured. Adjust the S-box, P-box, and number of rounds as per your requirements for differential analysis.