collision_attack

class cryptographic_estimators.UOVEstimator.UOVAlgorithms.collision_attack.CollisionAttack(problem: UOVProblem, **kwargs)

Bases: UOVAlgorithm

Construct an instance of CollisionAttack estimator.

Collision attack is a general attack which works against any signature which follows the hash-and-sign paradigm.

Parameters:

  • problem (UOVProblem) – An instance of the UOVProblem class

  • **kwargs –

    Additional keyword arguments

    gray_code_eval_cost (float) - Logarithm of the cost to evaluate one polynomial in one vector using Gray-code enumeration (default: log(q))

    X (int) - Number of preimages

    Y (int) - Number of variables in the salt space

    memory_access (int) - Specifies the memory access cost model (default: 0, choices: 0 - constant, 1 - logarithmic, 2 - square-root, 3 - cube-root or deploy custom function which takes as input the logarithm of the total memory usage)

    complexity_type (int) - Complexity type to consider (0: estimate, 1: tilde O complexity, default: 0)

X()

Return the optimal X, i.e. number of inputs (preimages).

Examples

>>> from cryptographic_estimators.UOVEstimator.UOVAlgorithms.collision_attack import CollisionAttack
>>> from cryptographic_estimators.UOVEstimator.uov_problem import UOVProblem
>>> E = CollisionAttack(UOVProblem(n=24, m=10, q=2))
>>> E.X()
11.958
Y()

Return logarithm of the optimal Y, i.e. logarithm of number of hashes to compute.

Examples

>>> from cryptographic_estimators.UOVEstimator.UOVAlgorithms.collision_attack import CollisionAttack
>>> from cryptographic_estimators.UOVEstimator.uov_problem import UOVProblem
>>> E = CollisionAttack(UOVProblem(n=24, m=10, q=2))
>>> E.Y()
0
property attack_type

Returns the attack type of the algorithm.

property complexity_type

Returns the attribute _complexity_type.

get_optimal_parameters_dict()

Returns the optimal parameters dictionary.

has_optimal_parameter()

Return True if the algorithm has optimal parameter.

Tests:
>>> from cryptographic_estimators import BaseAlgorithm, BaseProblem
>>> BaseAlgorithm(BaseProblem()).has_optimal_parameter()
False
linear_algebra_constant()

Return the linear algebra constant.

Tests:
>>> from cryptographic_estimators.UOVEstimator.uov_algorithm import UOVAlgorithm
>>> from cryptographic_estimators.UOVEstimator.uov_problem import UOVProblem
>>> UOVAlgorithm(UOVProblem(n=10, m=5, q=4), w=2).linear_algebra_constant()
2
property memory_access

Returns the attribute _memory_access.

memory_access_cost(mem: float)

Returns the memory access cost (in logarithmic scale) of the algorithm per basic operation.

Parameters:

mem (float) – Memory consumption of an algorithm.

Returns:

Memory access cost in logarithmic scale.

Return type:

float

Note

memory_access: Specifies the memory access cost model (default: 0, choices: 0 - constant, 1 - logarithmic, 2 - square-root, 3 - cube-root or deploy custom function which takes as input the logarithm of the total memory usage)

memory_complexity(**kwargs)

Return the memory complexity of the algorithm.

Parameters:

**kwargs –

Arbitrary keyword arguments.

optimal_parameters - If for each optimal parameter of the algorithm a value is provided, the computation is done based on those parameters.

optimal_parameters()

Return a dictionary of optimal parameters.

Tests:
>>> from cryptographic_estimators import BaseAlgorithm, BaseProblem
>>> BaseAlgorithm(BaseProblem()).optimal_parameters()
{}
parameter_names()

Return the list with the names of the algorithm’s parameters.

Tests:
>>> from cryptographic_estimators import BaseAlgorithm, BaseProblem
>>> BaseAlgorithm(BaseProblem()).parameter_names()
[]
property parameter_ranges

Returns the set ranges for optimal parameter search.

Returns the set ranges in which optimal parameters are searched by the optimization algorithm (used only for complexity type estimate).

quantum_time_complexity()

Return quantum gate complexity

reset()

Resets internal state of the algorithm.

set_parameter_ranges(parameter: str, min_value: float, max_value: float)

Set range of specific parameter.

If optimal parameter is already set, it must fall in that range.

Parameters:

  • parameter (str) – Name of parameter to set

  • min_value (float) – Lowerbound for parameter (inclusive)

  • max_value (float) – Upperbound for parameter (inclusive)

set_parameters(parameters: dict)

Set optimal parameters to predifined values.

Parameters:

parameters (dict) – Dictionary including parameters to set (for a subset of optimal_parameters functions)

time_complexity(**kwargs)

Return the time complexity of the algorithm.

Parameters:

**kwargs –

Arbitrary keyword arguments.

optimal_parameters - If for each optimal parameter of the algorithm a value is provided, the computation is done based on those parameters.