opendp.accuracy module#
- opendp.accuracy.accuracy_to_discrete_gaussian_scale(accuracy, alpha, T=None)[source]#
Convert a desired
accuracy
(tolerance) into a discrete gaussian noise scale at a statistical significance levelalpha
.accuracy_to_discrete_gaussian_scale in Rust documentation.
Proof Definition:
- Parameters:
accuracy – Desired accuracy. A tolerance for how far values may diverge from the input to the mechanism.
alpha – Statistical significance, level-
alpha
, or (1. -alpha
)100% confidence. Must be within (0, 1].T (Type Argument) – Data type of
accuracy
andalpha
- Return type:
Any
- Raises:
TypeError – if an argument’s type differs from the expected type
UnknownTypeError – if a type argument fails to parse
OpenDPException – packaged error from the core OpenDP library
- opendp.accuracy.accuracy_to_discrete_laplacian_scale(accuracy, alpha, T=None)[source]#
Convert a desired
accuracy
(tolerance) into a discrete Laplacian noise scale at a statistical significance levelalpha
.accuracy_to_discrete_laplacian_scale in Rust documentation.
Proof Definition:
- Parameters:
accuracy – Desired accuracy. A tolerance for how far values may diverge from the input to the mechanism.
alpha – Statistical significance, level-
alpha
, or (1. -alpha
)100% confidence. Must be within (0, 1].T (Type Argument) – Data type of
accuracy
andalpha
- Returns:
Discrete laplacian noise scale that meets the
accuracy
requirement at a given level-alpha
.- Return type:
Any
- Raises:
TypeError – if an argument’s type differs from the expected type
UnknownTypeError – if a type argument fails to parse
OpenDPException – packaged error from the core OpenDP library
- opendp.accuracy.accuracy_to_gaussian_scale(accuracy, alpha, T=None)[source]#
Convert a desired
accuracy
(tolerance) into a gaussian noise scale at a statistical significance levelalpha
.accuracy_to_gaussian_scale in Rust documentation.
- Parameters:
accuracy – Desired accuracy. A tolerance for how far values may diverge from the input to the mechanism.
alpha – Statistical significance, level-
alpha
, or (1. -alpha
)100% confidence. Must be within (0, 1].T (Type Argument) – Data type of
accuracy
andalpha
- Return type:
Any
- Raises:
TypeError – if an argument’s type differs from the expected type
UnknownTypeError – if a type argument fails to parse
OpenDPException – packaged error from the core OpenDP library
- opendp.accuracy.accuracy_to_laplacian_scale(accuracy, alpha, T=None)[source]#
Convert a desired
accuracy
(tolerance) into a Laplacian noise scale at a statistical significance levelalpha
.accuracy_to_laplacian_scale in Rust documentation.
- Parameters:
accuracy – Desired accuracy. A tolerance for how far values may diverge from the input to the mechanism.
alpha – Statistical significance, level-
alpha
, or (1. -alpha
)100% confidence. Must be within (0, 1].T (Type Argument) – Data type of
accuracy
andalpha
- Returns:
Laplacian noise scale that meets the
accuracy
requirement at a given level-alpha
.- Return type:
Any
- Raises:
TypeError – if an argument’s type differs from the expected type
UnknownTypeError – if a type argument fails to parse
OpenDPException – packaged error from the core OpenDP library
- opendp.accuracy.discrete_gaussian_scale_to_accuracy(scale, alpha, T=None)[source]#
Convert a discrete gaussian scale into an accuracy estimate (tolerance) at a statistical significance level
alpha
.discrete_gaussian_scale_to_accuracy in Rust documentation.
Proof Definition:
- Parameters:
scale – Gaussian noise scale.
alpha – Statistical significance, level-
alpha
, or (1. -alpha
)100% confidence. Must be within (0, 1].T (Type Argument) – Data type of
scale
andalpha
- Return type:
Any
- Raises:
TypeError – if an argument’s type differs from the expected type
UnknownTypeError – if a type argument fails to parse
OpenDPException – packaged error from the core OpenDP library
- opendp.accuracy.discrete_laplacian_scale_to_accuracy(scale, alpha, T=None)[source]#
Convert a discrete Laplacian scale into an accuracy estimate (tolerance) at a statistical significance level
alpha
.\(lpha = P[Y \ge accuracy]\), where \(Y = |X - z|\), and \(X \sim \mathcal{L}_{Z}(0, scale)\). That is, \(X\) is a discrete Laplace random variable and \(Y\) is the distribution of the errors.
This function returns a float accuracy. You can take the floor without affecting the coverage probability.
discrete_laplacian_scale_to_accuracy in Rust documentation.
Proof Definition:
- Parameters:
scale – Discrete Laplacian noise scale.
alpha – Statistical significance, level-
alpha
, or (1. -alpha
)100% confidence. Must be within (0, 1].T (Type Argument) – Data type of
scale
andalpha
- Return type:
Any
- Raises:
TypeError – if an argument’s type differs from the expected type
UnknownTypeError – if a type argument fails to parse
OpenDPException – packaged error from the core OpenDP library
- opendp.accuracy.gaussian_scale_to_accuracy(scale, alpha, T=None)[source]#
Convert a gaussian scale into an accuracy estimate (tolerance) at a statistical significance level
alpha
.gaussian_scale_to_accuracy in Rust documentation.
- Parameters:
scale – Gaussian noise scale.
alpha – Statistical significance, level-
alpha
, or (1. -alpha
)100% confidence. Must be within (0, 1].T (Type Argument) – Data type of
scale
andalpha
- Return type:
Any
- Raises:
TypeError – if an argument’s type differs from the expected type
UnknownTypeError – if a type argument fails to parse
OpenDPException – packaged error from the core OpenDP library
- opendp.accuracy.laplacian_scale_to_accuracy(scale, alpha, T=None)[source]#
Convert a Laplacian scale into an accuracy estimate (tolerance) at a statistical significance level
alpha
.laplacian_scale_to_accuracy in Rust documentation.
- Parameters:
scale – Laplacian noise scale.
alpha – Statistical significance, level-
alpha
, or (1. -alpha
)100% confidence. Must be within (0, 1].T (Type Argument) – Data type of
scale
andalpha
- Return type:
Any
- Raises:
TypeError – if an argument’s type differs from the expected type
UnknownTypeError – if a type argument fails to parse
OpenDPException – packaged error from the core OpenDP library