leebrickell¶

class cryptographic_estimators.SDFqEstimator.SDFqAlgorithms.leebrickell.LeeBrickell(problem: SDFqProblem, **kwargs)¶

Bases: SDFqAlgorithm

Construct an instance of Lee-Brickell’s estimator [LB88].

Expected weight distribution:

<———-+ n - k +———>
w-p

<———-+ k +————>
p

Parameters:: problem (SDFqProblem) – An SDProblem object including all necessary parameters.

Tests:

>>> from cryptographic_estimators.SDFqEstimator.SDFqAlgorithms import LeeBrickell
>>> from cryptographic_estimators.SDFqEstimator import SDFqProblem
>>> LeeBrickell(SDFqProblem(n=961,k=771,w=48,q=31)).time_complexity()
140.31928490910389

Examples

>>> from cryptographic_estimators.SDFqEstimator.SDFqAlgorithms import LeeBrickell
>>> from cryptographic_estimators.SDFqEstimator import SDFqProblem
>>> LeeBrickell(SDFqProblem(n=100,k=50,w=10,q=5))
Lee-Brickell estimator for syndrome decoding problem with (n,k,w) = (100,50,10) over Finite Field of size 5

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

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()
{}

p()¶

Returns the optimal parameter $p$ used in the algorithm optimization.

Examples

>>> from cryptographic_estimators.SDFqEstimator.SDFqAlgorithms import LeeBrickell
>>> from cryptographic_estimators.SDFqEstimator import SDFqProblem
>>> A = LeeBrickell(SDFqProblem(n=100,k=50,w=10,q=5))
>>> A.p()
2

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).

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.