wedge_attack¶

class cryptographic_estimators.UOVEstimator.UOVAlgorithms.wedge_attack.WedgeAttack(problem: UOVProblem, **kwargs)¶

Bases: UOVAlgorithm

Construct an instance of WedgeAttack estimator.

The wedge attack is a key-recovery attack introduced by Larns Ran in [Ran24].

Parameters:

problem (UOVProblem) – Object including all necessary parameters
w – Linear algebra constant (default: obtained from UOVAlgorithm)
h (int, optional) – External hybridization parameter. Defaults to 0.
memory_access (int, optional) – Specifies the memory access cost model. Defaults to 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, optional) – Complexity type to consider. Defaults to 0 (estimate).
bit_complexities (int, optional) – Determines if complexity is given in bit operations or basic operations. Defaults to 1 (in bit).

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.

o_prime()¶

Return the optimal parameter k used in the algorithm optimization.

>>> from cryptographic_estimators.UOVEstimator.uov_problem import UOVProblem
>>> from cryptographic_estimators.UOVEstimator.UOVAlgorithms.wedge_attack import WedgeAttack
>>> E = WedgeAttack(UOVProblem(n=24, m=10, q=2))
>>> E.o_prime()
7

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.