# Auto-generated. Do not edit.
from opendp._convert import *
from opendp._lib import *
from opendp.mod import *
from opendp.typing import *
from opendp.core import *
from opendp.domains import *
from opendp.metrics import *
from opendp.measures import *
__all__ = [
"make_base_discrete_gaussian",
"make_base_discrete_laplace",
"make_base_discrete_laplace_cks20",
"make_base_discrete_laplace_linear",
"make_base_gaussian",
"make_base_geometric",
"make_base_laplace",
"make_base_laplace_threshold",
"make_gaussian",
"make_laplace",
"make_randomized_response",
"make_randomized_response_bool",
"make_user_measurement",
"then_base_discrete_gaussian",
"then_base_discrete_laplace",
"then_base_discrete_laplace_cks20",
"then_base_discrete_laplace_linear",
"then_base_gaussian",
"then_base_geometric",
"then_base_laplace",
"then_base_laplace_threshold",
"then_gaussian",
"then_laplace",
"then_user_measurement"
]
[docs]
@versioned
def make_base_discrete_gaussian(
input_domain,
input_metric,
scale,
MO: RuntimeTypeDescriptor = "ZeroConcentratedDivergence<QO>"
) -> Measurement:
"""Make a Measurement that adds noise from the discrete_gaussian(`scale`) distribution to the input.
Valid inputs for `input_domain` and `input_metric` are:
| `input_domain` | input type | `input_metric` |
| ------------------------------- | ------------ | ---------------------- |
| `atom_domain(T)` | `T` | `absolute_distance(QI)` |
| `vector_domain(atom_domain(T))` | `Vec<T>` | `l2_distance(QI)` |
[make_base_discrete_gaussian in Rust documentation.](https://docs.rs/opendp/latest/opendp/measurements/fn.make_base_discrete_gaussian.html)
**Supporting Elements:**
* Input Domain: `D`
* Output Type: `D::Carrier`
* Input Metric: `D::InputMetric`
* Output Measure: `MO`
:param input_domain: Domain of the data type to be privatized.
:param input_metric: Metric of the data type to be privatized.
:param scale: Noise scale parameter for the gaussian distribution. `scale` == standard_deviation.
:param MO: Output measure. The only valid measure is `ZeroConcentratedDivergence<QO>`, but QO can be any float.
:type MO: :py:ref:`RuntimeTypeDescriptor`
:rtype: Measurement
:raises TypeError: if an argument's type differs from the expected type
:raises UnknownTypeError: if a type argument fails to parse
:raises OpenDPException: packaged error from the core OpenDP library
"""
assert_features("contrib")
# Standardize type arguments.
MO = RuntimeType.parse(type_name=MO, generics=["QO"])
QO = get_atom_or_infer(MO, scale)
MO = MO.substitute(QO=QO)
# Convert arguments to c types.
c_input_domain = py_to_c(input_domain, c_type=Domain, type_name=None)
c_input_metric = py_to_c(input_metric, c_type=Metric, type_name=None)
c_scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=QO)
c_MO = py_to_c(MO, c_type=ctypes.c_char_p)
# Call library function.
lib_function = lib.opendp_measurements__make_base_discrete_gaussian
lib_function.argtypes = [Domain, Metric, ctypes.c_void_p, ctypes.c_char_p]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_input_domain, c_input_metric, c_scale, c_MO), Measurement))
return output
[docs]
def then_base_discrete_gaussian(
scale,
MO: RuntimeTypeDescriptor = "ZeroConcentratedDivergence<QO>"
):
return PartialConstructor(lambda input_domain, input_metric: make_base_discrete_gaussian(
input_domain=input_domain,
input_metric=input_metric,
scale=scale,
MO=MO))
[docs]
@versioned
def make_base_discrete_laplace(
input_domain,
input_metric,
scale,
QO: RuntimeTypeDescriptor = None
) -> Measurement:
"""Make a Measurement that adds noise from the discrete_laplace(`scale`) distribution to the input.
Valid inputs for `input_domain` and `input_metric` are:
| `input_domain` | input type | `input_metric` |
| ------------------------------- | ------------ | ---------------------- |
| `atom_domain(T)` (default) | `T` | `absolute_distance(T)` |
| `vector_domain(atom_domain(T))` | `Vec<T>` | `l1_distance(T)` |
This uses `make_base_discrete_laplace_cks20` if scale is greater than 10, otherwise it uses `make_base_discrete_laplace_linear`.
[make_base_discrete_laplace in Rust documentation.](https://docs.rs/opendp/latest/opendp/measurements/fn.make_base_discrete_laplace.html)
**Citations:**
* [GRS12 Universally Utility-Maximizing Privacy Mechanisms](https://theory.stanford.edu/~tim/papers/priv.pdf)
* [CKS20 The Discrete Gaussian for Differential Privacy](https://arxiv.org/pdf/2004.00010.pdf#subsection.5.2)
**Supporting Elements:**
* Input Domain: `D`
* Output Type: `D::Carrier`
* Input Metric: `D::InputMetric`
* Output Measure: `MaxDivergence<QO>`
:param input_domain: Domain of the data type to be privatized.
:param input_metric: Metric of the data type to be privatized.
:param scale: Noise scale parameter for the laplace distribution. `scale` == standard_deviation / sqrt(2).
:param QO: Data type of the output distance and scale. `f32` or `f64`.
:type QO: :py:ref:`RuntimeTypeDescriptor`
:rtype: Measurement
:raises TypeError: if an argument's type differs from the expected type
:raises UnknownTypeError: if a type argument fails to parse
:raises OpenDPException: packaged error from the core OpenDP library
"""
assert_features("contrib")
# Standardize type arguments.
QO = RuntimeType.parse_or_infer(type_name=QO, public_example=scale)
# Convert arguments to c types.
c_input_domain = py_to_c(input_domain, c_type=Domain, type_name=None)
c_input_metric = py_to_c(input_metric, c_type=Metric, type_name=None)
c_scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=QO)
c_QO = py_to_c(QO, c_type=ctypes.c_char_p)
# Call library function.
lib_function = lib.opendp_measurements__make_base_discrete_laplace
lib_function.argtypes = [Domain, Metric, ctypes.c_void_p, ctypes.c_char_p]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_input_domain, c_input_metric, c_scale, c_QO), Measurement))
return output
[docs]
def then_base_discrete_laplace(
scale,
QO: RuntimeTypeDescriptor = None
):
return PartialConstructor(lambda input_domain, input_metric: make_base_discrete_laplace(
input_domain=input_domain,
input_metric=input_metric,
scale=scale,
QO=QO))
[docs]
@versioned
def make_base_discrete_laplace_cks20(
input_domain,
input_metric,
scale,
QO: RuntimeTypeDescriptor = None
) -> Measurement:
"""Make a Measurement that adds noise from the discrete_laplace(`scale`) distribution to the input,
using an efficient algorithm on rational bignums.
Valid inputs for `input_domain` and `input_metric` are:
| `input_domain` | input type | `input_metric` |
| ------------------------------- | ------------ | ---------------------- |
| `atom_domain(T)` (default) | `T` | `absolute_distance(T)` |
| `vector_domain(atom_domain(T))` | `Vec<T>` | `l1_distance(T)` |
[make_base_discrete_laplace_cks20 in Rust documentation.](https://docs.rs/opendp/latest/opendp/measurements/fn.make_base_discrete_laplace_cks20.html)
**Citations:**
* [CKS20 The Discrete Gaussian for Differential Privacy](https://arxiv.org/pdf/2004.00010.pdf#subsection.5.2)
**Supporting Elements:**
* Input Domain: `D`
* Output Type: `D::Carrier`
* Input Metric: `D::InputMetric`
* Output Measure: `MaxDivergence<QO>`
:param input_domain:
:param input_metric:
:param scale: Noise scale parameter for the laplace distribution. `scale` == standard_deviation / sqrt(2).
:param QO: Data type of the output distance and scale.
:type QO: :py:ref:`RuntimeTypeDescriptor`
:rtype: Measurement
:raises TypeError: if an argument's type differs from the expected type
:raises UnknownTypeError: if a type argument fails to parse
:raises OpenDPException: packaged error from the core OpenDP library
"""
assert_features("contrib")
# Standardize type arguments.
QO = RuntimeType.parse_or_infer(type_name=QO, public_example=scale)
# Convert arguments to c types.
c_input_domain = py_to_c(input_domain, c_type=Domain, type_name=None)
c_input_metric = py_to_c(input_metric, c_type=Metric, type_name=None)
c_scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=QO)
c_QO = py_to_c(QO, c_type=ctypes.c_char_p)
# Call library function.
lib_function = lib.opendp_measurements__make_base_discrete_laplace_cks20
lib_function.argtypes = [Domain, Metric, ctypes.c_void_p, ctypes.c_char_p]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_input_domain, c_input_metric, c_scale, c_QO), Measurement))
return output
[docs]
def then_base_discrete_laplace_cks20(
scale,
QO: RuntimeTypeDescriptor = None
):
return PartialConstructor(lambda input_domain, input_metric: make_base_discrete_laplace_cks20(
input_domain=input_domain,
input_metric=input_metric,
scale=scale,
QO=QO))
[docs]
@versioned
def make_base_discrete_laplace_linear(
input_domain,
input_metric,
scale,
bounds: Any = None,
QO: RuntimeTypeDescriptor = None
) -> Measurement:
"""Make a Measurement that adds noise from the discrete_laplace(`scale`) distribution to the input,
using a linear-time algorithm on finite data types.
This algorithm can be executed in constant time if bounds are passed.
Valid inputs for `input_domain` and `input_metric` are:
| `input_domain` | input type | `input_metric` |
| ------------------------------- | ------------ | ---------------------- |
| `atom_domain(T)` (default) | `T` | `absolute_distance(T)` |
| `vector_domain(atom_domain(T))` | `Vec<T>` | `l1_distance(T)` |
[make_base_discrete_laplace_linear in Rust documentation.](https://docs.rs/opendp/latest/opendp/measurements/fn.make_base_discrete_laplace_linear.html)
**Citations:**
* [GRS12 Universally Utility-Maximizing Privacy Mechanisms](https://theory.stanford.edu/~tim/papers/priv.pdf)
**Supporting Elements:**
* Input Domain: `D`
* Output Type: `D::Carrier`
* Input Metric: `D::InputMetric`
* Output Measure: `MaxDivergence<QO>`
:param input_domain: Domain of the data type to be privatized.
:param input_metric: Metric of the data type to be privatized.
:param scale: Noise scale parameter for the distribution. `scale` == standard_deviation / sqrt(2).
:param bounds: Set bounds on the count to make the algorithm run in constant-time.
:type bounds: Any
:param QO: Data type of the scale and output distance.
:type QO: :py:ref:`RuntimeTypeDescriptor`
:rtype: Measurement
:raises TypeError: if an argument's type differs from the expected type
:raises UnknownTypeError: if a type argument fails to parse
:raises OpenDPException: packaged error from the core OpenDP library
"""
assert_features("contrib")
# Standardize type arguments.
QO = RuntimeType.parse_or_infer(type_name=QO, public_example=scale)
T = get_atom(get_carrier_type(input_domain))
OptionT = RuntimeType(origin='Option', args=[RuntimeType(origin='Tuple', args=[T, T])])
# Convert arguments to c types.
c_input_domain = py_to_c(input_domain, c_type=Domain, type_name=None)
c_input_metric = py_to_c(input_metric, c_type=Metric, type_name=None)
c_scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=QO)
c_bounds = py_to_c(bounds, c_type=AnyObjectPtr, type_name=OptionT)
c_QO = py_to_c(QO, c_type=ctypes.c_char_p)
# Call library function.
lib_function = lib.opendp_measurements__make_base_discrete_laplace_linear
lib_function.argtypes = [Domain, Metric, ctypes.c_void_p, AnyObjectPtr, ctypes.c_char_p]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_input_domain, c_input_metric, c_scale, c_bounds, c_QO), Measurement))
return output
[docs]
def then_base_discrete_laplace_linear(
scale,
bounds: Any = None,
QO: RuntimeTypeDescriptor = None
):
return PartialConstructor(lambda input_domain, input_metric: make_base_discrete_laplace_linear(
input_domain=input_domain,
input_metric=input_metric,
scale=scale,
bounds=bounds,
QO=QO))
[docs]
@versioned
def make_base_gaussian(
input_domain,
input_metric,
scale,
k: int = -1074,
MO: RuntimeTypeDescriptor = "ZeroConcentratedDivergence<T>"
) -> Measurement:
"""Make a Measurement that adds noise from the gaussian(`scale`) distribution to the input.
Valid inputs for `input_domain` and `input_metric` are:
| `input_domain` | input type | `input_metric` |
| ------------------------------- | ------------ | ---------------------- |
| `atom_domain(T)` (default) | `T` | `absolute_distance(T)` |
| `vector_domain(atom_domain(T))` | `Vec<T>` | `l2_distance(T)` |
This function takes a noise granularity in terms of 2^k.
Larger granularities are more computationally efficient, but have a looser privacy map.
If k is not set, k defaults to the smallest granularity.
[make_base_gaussian in Rust documentation.](https://docs.rs/opendp/latest/opendp/measurements/fn.make_base_gaussian.html)
**Supporting Elements:**
* Input Domain: `D`
* Output Type: `D::Carrier`
* Input Metric: `D::InputMetric`
* Output Measure: `MO`
:param input_domain: Domain of the data type to be privatized. Valid values are `VectorDomain<AtomDomain<T>>` or `AtomDomain<T>`.
:param input_metric: Metric of the data type to be privatized. Valid values are `AbsoluteDistance<T>` or `L2Distance<T>`.
:param scale: Noise scale parameter for the gaussian distribution. `scale` == standard_deviation.
:param k: The noise granularity in terms of 2^k.
:type k: int
:param MO: Output Measure. The only valid measure is `ZeroConcentratedDivergence<T>`.
:type MO: :py:ref:`RuntimeTypeDescriptor`
:rtype: Measurement
:raises TypeError: if an argument's type differs from the expected type
:raises UnknownTypeError: if a type argument fails to parse
:raises OpenDPException: packaged error from the core OpenDP library
"""
assert_features("contrib")
# Standardize type arguments.
MO = RuntimeType.parse(type_name=MO, generics=["T"])
T = get_atom_or_infer(get_carrier_type(input_domain), scale)
MO = MO.substitute(T=T)
# Convert arguments to c types.
c_input_domain = py_to_c(input_domain, c_type=Domain, type_name=None)
c_input_metric = py_to_c(input_metric, c_type=Metric, type_name=None)
c_scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=T)
c_k = py_to_c(k, c_type=ctypes.c_uint32, type_name=i32)
c_MO = py_to_c(MO, c_type=ctypes.c_char_p)
# Call library function.
lib_function = lib.opendp_measurements__make_base_gaussian
lib_function.argtypes = [Domain, Metric, ctypes.c_void_p, ctypes.c_uint32, ctypes.c_char_p]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_input_domain, c_input_metric, c_scale, c_k, c_MO), Measurement))
return output
[docs]
def then_base_gaussian(
scale,
k: int = -1074,
MO: RuntimeTypeDescriptor = "ZeroConcentratedDivergence<T>"
):
return PartialConstructor(lambda input_domain, input_metric: make_base_gaussian(
input_domain=input_domain,
input_metric=input_metric,
scale=scale,
k=k,
MO=MO))
[docs]
@versioned
def make_base_geometric(
input_domain,
input_metric,
scale,
bounds: Any = None,
QO: RuntimeTypeDescriptor = None
) -> Measurement:
"""An alias for `make_base_discrete_laplace_linear`.
If you don't need timing side-channel protections via `bounds`,
`make_base_discrete_laplace` is more efficient.
[make_base_geometric in Rust documentation.](https://docs.rs/opendp/latest/opendp/measurements/fn.make_base_geometric.html)
**Supporting Elements:**
* Input Domain: `D`
* Output Type: `D::Carrier`
* Input Metric: `D::InputMetric`
* Output Measure: `MaxDivergence<QO>`
:param input_domain:
:param input_metric:
:param scale:
:param bounds:
:type bounds: Any
:param QO:
:type QO: :py:ref:`RuntimeTypeDescriptor`
:rtype: Measurement
:raises TypeError: if an argument's type differs from the expected type
:raises UnknownTypeError: if a type argument fails to parse
:raises OpenDPException: packaged error from the core OpenDP library
"""
assert_features("contrib")
# Standardize type arguments.
QO = RuntimeType.parse_or_infer(type_name=QO, public_example=scale)
T = get_atom(get_carrier_type(input_domain))
OptionT = RuntimeType(origin='Option', args=[RuntimeType(origin='Tuple', args=[T, T])])
# Convert arguments to c types.
c_input_domain = py_to_c(input_domain, c_type=Domain, type_name=None)
c_input_metric = py_to_c(input_metric, c_type=Metric, type_name=None)
c_scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=QO)
c_bounds = py_to_c(bounds, c_type=AnyObjectPtr, type_name=OptionT)
c_QO = py_to_c(QO, c_type=ctypes.c_char_p)
# Call library function.
lib_function = lib.opendp_measurements__make_base_geometric
lib_function.argtypes = [Domain, Metric, ctypes.c_void_p, AnyObjectPtr, ctypes.c_char_p]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_input_domain, c_input_metric, c_scale, c_bounds, c_QO), Measurement))
return output
[docs]
def then_base_geometric(
scale,
bounds: Any = None,
QO: RuntimeTypeDescriptor = None
):
return PartialConstructor(lambda input_domain, input_metric: make_base_geometric(
input_domain=input_domain,
input_metric=input_metric,
scale=scale,
bounds=bounds,
QO=QO))
[docs]
@versioned
def make_base_laplace(
input_domain,
input_metric,
scale,
k: int = -1074
) -> Measurement:
"""Make a Measurement that adds noise from the Laplace(`scale`) distribution to a scalar value.
Valid inputs for `input_domain` and `input_metric` are:
| `input_domain` | input type | `input_metric` |
| ------------------------------- | ------------ | ---------------------- |
| `atom_domain(T)` (default) | `T` | `absolute_distance(T)` |
| `vector_domain(atom_domain(T))` | `Vec<T>` | `l1_distance(T)` |
This function takes a noise granularity in terms of 2^k.
Larger granularities are more computationally efficient, but have a looser privacy map.
If k is not set, k defaults to the smallest granularity.
[make_base_laplace in Rust documentation.](https://docs.rs/opendp/latest/opendp/measurements/fn.make_base_laplace.html)
**Supporting Elements:**
* Input Domain: `D`
* Output Type: `D::Carrier`
* Input Metric: `D::InputMetric`
* Output Measure: `MaxDivergence<D::Atom>`
:param input_domain: Domain of the data type to be privatized.
:param input_metric: Metric of the data type to be privatized.
:param scale: Noise scale parameter for the laplace distribution. `scale` == standard_deviation / sqrt(2).
:param k: The noise granularity in terms of 2^k.
:type k: int
:rtype: Measurement
:raises TypeError: if an argument's type differs from the expected type
:raises UnknownTypeError: if a type argument fails to parse
:raises OpenDPException: packaged error from the core OpenDP library
"""
assert_features("contrib")
# Standardize type arguments.
T = get_atom_or_infer(get_carrier_type(input_domain), scale)
# Convert arguments to c types.
c_input_domain = py_to_c(input_domain, c_type=Domain, type_name=None)
c_input_metric = py_to_c(input_metric, c_type=Metric, type_name=None)
c_scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=T)
c_k = py_to_c(k, c_type=ctypes.c_uint32, type_name=i32)
# Call library function.
lib_function = lib.opendp_measurements__make_base_laplace
lib_function.argtypes = [Domain, Metric, ctypes.c_void_p, ctypes.c_uint32]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_input_domain, c_input_metric, c_scale, c_k), Measurement))
return output
[docs]
def then_base_laplace(
scale,
k: int = -1074
):
return PartialConstructor(lambda input_domain, input_metric: make_base_laplace(
input_domain=input_domain,
input_metric=input_metric,
scale=scale,
k=k))
[docs]
@versioned
def make_base_laplace_threshold(
input_domain,
input_metric,
scale,
threshold,
k: int = -1074
) -> Measurement:
"""Make a Measurement that uses propose-test-release to privatize a hashmap of counts.
This function takes a noise granularity in terms of 2^k.
Larger granularities are more computationally efficient, but have a looser privacy map.
If k is not set, k defaults to the smallest granularity.
[make_base_laplace_threshold in Rust documentation.](https://docs.rs/opendp/latest/opendp/measurements/fn.make_base_laplace_threshold.html)
**Supporting Elements:**
* Input Domain: `MapDomain<AtomDomain<TK>, AtomDomain<TV>>`
* Output Type: `HashMap<TK, TV>`
* Input Metric: `L1Distance<TV>`
* Output Measure: `FixedSmoothedMaxDivergence<TV>`
:param input_domain: Domain of the input.
:param input_metric: Metric for the input domain.
:param scale: Noise scale parameter for the laplace distribution. `scale` == standard_deviation / sqrt(2).
:param threshold: Exclude counts that are less than this minimum value.
:param k: The noise granularity in terms of 2^k.
:type k: int
:rtype: Measurement
:raises TypeError: if an argument's type differs from the expected type
:raises UnknownTypeError: if a type argument fails to parse
:raises OpenDPException: packaged error from the core OpenDP library
"""
assert_features("contrib", "floating-point")
# Standardize type arguments.
TV = get_distance_type(input_metric)
# Convert arguments to c types.
c_input_domain = py_to_c(input_domain, c_type=Domain, type_name=None)
c_input_metric = py_to_c(input_metric, c_type=Metric, type_name=None)
c_scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=TV)
c_threshold = py_to_c(threshold, c_type=ctypes.c_void_p, type_name=TV)
c_k = py_to_c(k, c_type=ctypes.c_uint32, type_name=i32)
# Call library function.
lib_function = lib.opendp_measurements__make_base_laplace_threshold
lib_function.argtypes = [Domain, Metric, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_uint32]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_input_domain, c_input_metric, c_scale, c_threshold, c_k), Measurement))
return output
[docs]
def then_base_laplace_threshold(
scale,
threshold,
k: int = -1074
):
return PartialConstructor(lambda input_domain, input_metric: make_base_laplace_threshold(
input_domain=input_domain,
input_metric=input_metric,
scale=scale,
threshold=threshold,
k=k))
[docs]
@versioned
def make_gaussian(
input_domain,
input_metric,
scale,
MO: RuntimeTypeDescriptor = "ZeroConcentratedDivergence<QO>"
) -> Measurement:
"""Make a Measurement that adds noise from the gaussian(`scale`) distribution to the input.
Valid inputs for `input_domain` and `input_metric` are:
| `input_domain` | input type | `input_metric` |
| ------------------------------- | ------------ | ----------------------- |
| `atom_domain(T)` | `T` | `absolute_distance(QI)` |
| `vector_domain(atom_domain(T))` | `Vec<T>` | `l2_distance(QI)` |
[make_gaussian in Rust documentation.](https://docs.rs/opendp/latest/opendp/measurements/fn.make_gaussian.html)
**Supporting Elements:**
* Input Domain: `D`
* Output Type: `D::Carrier`
* Input Metric: `D::InputMetric`
* Output Measure: `MO`
:param input_domain: Domain of the data type to be privatized.
:param input_metric: Metric of the data type to be privatized.
:param scale: Noise scale parameter for the gaussian distribution. `scale` == standard_deviation.
:param MO: Output Measure. The only valid measure is `ZeroConcentratedDivergence<T>`.
:type MO: :py:ref:`RuntimeTypeDescriptor`
:rtype: Measurement
:raises TypeError: if an argument's type differs from the expected type
:raises UnknownTypeError: if a type argument fails to parse
:raises OpenDPException: packaged error from the core OpenDP library
"""
assert_features("contrib")
# Standardize type arguments.
MO = RuntimeType.parse(type_name=MO, generics=["QO"])
QO = get_atom_or_infer(MO, scale)
MO = MO.substitute(QO=QO)
# Convert arguments to c types.
c_input_domain = py_to_c(input_domain, c_type=Domain, type_name=None)
c_input_metric = py_to_c(input_metric, c_type=Metric, type_name=None)
c_scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=get_atom(MO))
c_MO = py_to_c(MO, c_type=ctypes.c_char_p)
# Call library function.
lib_function = lib.opendp_measurements__make_gaussian
lib_function.argtypes = [Domain, Metric, ctypes.c_void_p, ctypes.c_char_p]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_input_domain, c_input_metric, c_scale, c_MO), Measurement))
return output
[docs]
def then_gaussian(
scale,
MO: RuntimeTypeDescriptor = "ZeroConcentratedDivergence<QO>"
):
return PartialConstructor(lambda input_domain, input_metric: make_gaussian(
input_domain=input_domain,
input_metric=input_metric,
scale=scale,
MO=MO))
[docs]
@versioned
def make_laplace(
input_domain,
input_metric,
scale,
QO: RuntimeTypeDescriptor = "float"
) -> Measurement:
"""Make a Measurement that adds noise from the laplace(`scale`) distribution to the input.
Valid inputs for `input_domain` and `input_metric` are:
| `input_domain` | input type | `input_metric` |
| ------------------------------- | ------------ | ---------------------- |
| `atom_domain(T)` (default) | `T` | `absolute_distance(T)` |
| `vector_domain(atom_domain(T))` | `Vec<T>` | `l1_distance(T)` |
This uses `make_base_laplace` if `T` is float, otherwise it uses `make_base_discrete_laplace`.
[make_laplace in Rust documentation.](https://docs.rs/opendp/latest/opendp/measurements/fn.make_laplace.html)
**Citations:**
* [GRS12 Universally Utility-Maximizing Privacy Mechanisms](https://theory.stanford.edu/~tim/papers/priv.pdf)
* [CKS20 The Discrete Gaussian for Differential Privacy](https://arxiv.org/pdf/2004.00010.pdf#subsection.5.2)
**Supporting Elements:**
* Input Domain: `D`
* Output Type: `D::Carrier`
* Input Metric: `D::InputMetric`
* Output Measure: `MaxDivergence<QO>`
:param input_domain: Domain of the data type to be privatized.
:param input_metric: Metric of the data type to be privatized.
:param scale: Noise scale parameter for the laplace distribution. `scale` == standard_deviation / sqrt(2).
:param QO: Data type of the output distance and scale. `f32` or `f64`.
:type QO: :py:ref:`RuntimeTypeDescriptor`
:rtype: Measurement
:raises TypeError: if an argument's type differs from the expected type
:raises UnknownTypeError: if a type argument fails to parse
:raises OpenDPException: packaged error from the core OpenDP library
"""
assert_features("contrib")
# Standardize type arguments.
QO = RuntimeType.parse(type_name=QO)
# Convert arguments to c types.
c_input_domain = py_to_c(input_domain, c_type=Domain, type_name=None)
c_input_metric = py_to_c(input_metric, c_type=Metric, type_name=None)
c_scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=get_atom(QO))
c_QO = py_to_c(QO, c_type=ctypes.c_char_p)
# Call library function.
lib_function = lib.opendp_measurements__make_laplace
lib_function.argtypes = [Domain, Metric, ctypes.c_void_p, ctypes.c_char_p]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_input_domain, c_input_metric, c_scale, c_QO), Measurement))
return output
[docs]
def then_laplace(
scale,
QO: RuntimeTypeDescriptor = "float"
):
return PartialConstructor(lambda input_domain, input_metric: make_laplace(
input_domain=input_domain,
input_metric=input_metric,
scale=scale,
QO=QO))
[docs]
@versioned
def make_randomized_response(
categories: Any,
prob,
constant_time: bool = False,
T: RuntimeTypeDescriptor = None,
QO: RuntimeTypeDescriptor = None
) -> Measurement:
"""Make a Measurement that implements randomized response on a categorical value.
[make_randomized_response in Rust documentation.](https://docs.rs/opendp/latest/opendp/measurements/fn.make_randomized_response.html)
**Supporting Elements:**
* Input Domain: `AtomDomain<T>`
* Output Type: `T`
* Input Metric: `DiscreteDistance`
* Output Measure: `MaxDivergence<QO>`
:param categories: Set of valid outcomes
:type categories: Any
:param prob: Probability of returning the correct answer. Must be in `[1/num_categories, 1)`
:param constant_time: Set to true to enable constant time. Slower.
:type constant_time: bool
:param T: Data type of a category.
:type T: :py:ref:`RuntimeTypeDescriptor`
:param QO: Data type of probability and output distance.
:type QO: :py:ref:`RuntimeTypeDescriptor`
:rtype: Measurement
:raises TypeError: if an argument's type differs from the expected type
:raises UnknownTypeError: if a type argument fails to parse
:raises OpenDPException: packaged error from the core OpenDP library
"""
assert_features("contrib")
# Standardize type arguments.
T = RuntimeType.parse_or_infer(type_name=T, public_example=get_first(categories))
QO = RuntimeType.parse_or_infer(type_name=QO, public_example=prob)
# Convert arguments to c types.
c_categories = py_to_c(categories, c_type=AnyObjectPtr, type_name=RuntimeType(origin='Vec', args=[T]))
c_prob = py_to_c(prob, c_type=ctypes.c_void_p, type_name=QO)
c_constant_time = py_to_c(constant_time, c_type=ctypes.c_bool, type_name=bool)
c_T = py_to_c(T, c_type=ctypes.c_char_p)
c_QO = py_to_c(QO, c_type=ctypes.c_char_p)
# Call library function.
lib_function = lib.opendp_measurements__make_randomized_response
lib_function.argtypes = [AnyObjectPtr, ctypes.c_void_p, ctypes.c_bool, ctypes.c_char_p, ctypes.c_char_p]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_categories, c_prob, c_constant_time, c_T, c_QO), Measurement))
return output
[docs]
@versioned
def make_randomized_response_bool(
prob,
constant_time: bool = False,
QO: RuntimeTypeDescriptor = None
) -> Measurement:
"""Make a Measurement that implements randomized response on a boolean value.
[make_randomized_response_bool in Rust documentation.](https://docs.rs/opendp/latest/opendp/measurements/fn.make_randomized_response_bool.html)
**Supporting Elements:**
* Input Domain: `AtomDomain<bool>`
* Output Type: `bool`
* Input Metric: `DiscreteDistance`
* Output Measure: `MaxDivergence<QO>`
:param prob: Probability of returning the correct answer. Must be in `[0.5, 1)`
:param constant_time: Set to true to enable constant time. Slower.
:type constant_time: bool
:param QO: Data type of probability and output distance.
:type QO: :py:ref:`RuntimeTypeDescriptor`
:rtype: Measurement
:raises TypeError: if an argument's type differs from the expected type
:raises UnknownTypeError: if a type argument fails to parse
:raises OpenDPException: packaged error from the core OpenDP library
"""
assert_features("contrib")
# Standardize type arguments.
QO = RuntimeType.parse_or_infer(type_name=QO, public_example=prob)
# Convert arguments to c types.
c_prob = py_to_c(prob, c_type=ctypes.c_void_p, type_name=QO)
c_constant_time = py_to_c(constant_time, c_type=ctypes.c_bool, type_name=bool)
c_QO = py_to_c(QO, c_type=ctypes.c_char_p)
# Call library function.
lib_function = lib.opendp_measurements__make_randomized_response_bool
lib_function.argtypes = [ctypes.c_void_p, ctypes.c_bool, ctypes.c_char_p]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_prob, c_constant_time, c_QO), Measurement))
return output
[docs]
@versioned
def make_user_measurement(
input_domain: Domain,
input_metric: Metric,
output_measure: Measure,
function,
privacy_map,
TO: RuntimeTypeDescriptor
) -> Measurement:
"""Construct a Measurement from user-defined callbacks.
[make_user_measurement in Rust documentation.](https://docs.rs/opendp/latest/opendp/measurements/fn.make_user_measurement.html)
**Supporting Elements:**
* Input Domain: `AnyDomain`
* Output Type: `AnyObject`
* Input Metric: `AnyMetric`
* Output Measure: `AnyMeasure`
:param input_domain: A domain describing the set of valid inputs for the function.
:type input_domain: Domain
:param input_metric: The metric from which distances between adjacent inputs are measured.
:type input_metric: Metric
:param output_measure: The measure from which distances between adjacent output distributions are measured.
:type output_measure: Measure
:param function: A function mapping data from `input_domain` to a release of type `TO`.
:param privacy_map: A function mapping distances from `input_metric` to `output_measure`.
:param TO: The data type of outputs from the function.
:type TO: :py:ref:`RuntimeTypeDescriptor`
:rtype: Measurement
:raises TypeError: if an argument's type differs from the expected type
:raises UnknownTypeError: if a type argument fails to parse
:raises OpenDPException: packaged error from the core OpenDP library
"""
assert_features("contrib", "honest-but-curious")
# Standardize type arguments.
TO = RuntimeType.parse(type_name=TO)
# Convert arguments to c types.
c_input_domain = py_to_c(input_domain, c_type=Domain, type_name=None)
c_input_metric = py_to_c(input_metric, c_type=Metric, type_name=None)
c_output_measure = py_to_c(output_measure, c_type=Measure, type_name=AnyMeasure)
c_function = py_to_c(function, c_type=CallbackFn, type_name=pass_through(TO))
c_privacy_map = py_to_c(privacy_map, c_type=CallbackFn, type_name=measure_distance_type(output_measure))
c_TO = py_to_c(TO, c_type=ctypes.c_char_p)
# Call library function.
lib_function = lib.opendp_measurements__make_user_measurement
lib_function.argtypes = [Domain, Metric, Measure, CallbackFn, CallbackFn, ctypes.c_char_p]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_input_domain, c_input_metric, c_output_measure, c_function, c_privacy_map, c_TO), Measurement))
output._depends_on(c_function, c_privacy_map)
return output
[docs]
def then_user_measurement(
output_measure: Measure,
function,
privacy_map,
TO: RuntimeTypeDescriptor
):
return PartialConstructor(lambda input_domain, input_metric: make_user_measurement(
input_domain=input_domain,
input_metric=input_metric,
output_measure=output_measure,
function=function,
privacy_map=privacy_map,
TO=TO))
