This documentation is for an old version of OpenDP.

The current release of OpenDP is v0.11.1.

Source code for opendp.mod

import ctypes
from typing import Union, Tuple, Callable

from opendp._lib import AnyMeasurement, AnyTransformation


[docs] class Measurement(ctypes.POINTER(AnyMeasurement)): """A differentially private unit of computation. A measurement contains a function and a privacy relation. The function releases a differentially-private release. The privacy relation maps from an input metric to an output measure. :example: >>> from opendp.mod import Measurement, enable_features >>> enable_features("contrib") ... >>> # create an instance of Measurement using a constructor from the meas module >>> from opendp.meas import make_base_geometric >>> base_geometric: Measurement = make_base_geometric(scale=2.) ... >>> # invoke the measurement (invoke and __call__ are equivalent) >>> base_geometric.invoke(100) # -> 101 # doctest: +SKIP >>> base_geometric(100) # -> 99 # doctest: +SKIP ... >>> # check the measurement's relation at >>> # (1, 0.5): (AbsoluteDistance<u32>, MaxDivergence) >>> assert base_geometric.check(1, 0.5) ... >>> # chain with a transformation from the trans module >>> from opendp.trans import make_count >>> chained = ( ... make_count(TIA=int) >> ... base_geometric ... ) ... >>> # the resulting measurement has the same features >>> chained([1, 2, 3]) # -> 4 # doctest: +SKIP >>> # check the chained measurement's relation at >>> # (1, 0.5): (SubstituteDistance, MaxDivergence) >>> assert chained.check(1, 0.5) """ _type_ = AnyMeasurement def __call__(self, arg): from opendp.core import measurement_invoke return measurement_invoke(self, arg)
[docs] def invoke(self, arg): """Create a differentially-private release with `arg`. :param arg: Input to the measurement. :return: differentially-private release :raises OpenDPException: packaged error from the core OpenDP library """ from opendp.core import measurement_invoke return measurement_invoke(self, arg)
[docs] def check(self, d_in, d_out, *, debug=False) -> bool: """Check if the measurement satisfies the privacy relation at `d_in`, `d_out`. :param d_in: Distance in terms of the input metric. :param d_out: Distance in terms of the output measure. :param debug: Enable to raise Exceptions to help identify why the privacy relation failed. :return: If True, a release is differentially private at `d_in`, `d_out`. :rtype: bool """ from opendp.core import measurement_check if debug: return measurement_check(self, d_in, d_out) try: return measurement_check(self, d_in, d_out) except OpenDPException as err: if err.variant == "RelationDebug": return False raise
@property def input_distance_type(self): """Retrieve the distance type of the input metric. This may be any integral type for dataset metrics, or any numeric type for sensitivity metrics. :return: distance type """ from opendp.core import measurement_input_distance_type from opendp.typing import RuntimeType return RuntimeType.parse(measurement_input_distance_type(self)) @property def output_distance_type(self): """Retrieve the distance type of the output measure. This is the type that the budget is expressed in. :return: distance type """ from opendp.typing import RuntimeType from opendp.core import measurement_output_distance_type return RuntimeType.parse(measurement_output_distance_type(self)) @property def input_carrier_type(self): """Retrieve the carrier type of the input domain. Any member of the input domain is a member of the carrier type. :return: carrier type """ from opendp.core import measurement_input_carrier_type from opendp.typing import RuntimeType return RuntimeType.parse(measurement_input_carrier_type(self)) def __del__(self): from opendp.core import _measurement_free _measurement_free(self)
[docs] class Transformation(ctypes.POINTER(AnyTransformation)): """A non-differentially private unit of computation. A transformation contains a function and a stability relation. The function maps from an input domain to an output domain. The stability relation maps from an input metric to an output metric. :example: >>> from opendp.mod import Transformation, enable_features >>> enable_features("contrib") ... >>> # create an instance of Transformation using a constructor from the trans module >>> from opendp.trans import make_count >>> count: Transformation = make_count(TIA=int) ... >>> # invoke the transformation (invoke and __call__ are equivalent) >>> count.invoke([1, 2, 3]) # -> 3 # doctest: +SKIP >>> count([1, 2, 3]) # -> 3 # doctest: +SKIP ... >>> # check the transformation's relation at >>> # (1, 1): (SymmetricDistance, AbsoluteDistance<u32>) >>> assert count.check(1, 1) ... >>> # chain with more transformations from the trans module >>> from opendp.trans import make_split_lines, make_cast, make_impute_constant >>> chained = ( ... make_split_lines() >> ... make_cast(TIA=str, TOA=int) >> ... make_impute_constant(constant=0) >> ... count ... ) ... >>> # the resulting transformation has the same features >>> chained("1\\n2\\n3") # -> 3 # doctest: +SKIP >>> assert chained.check(1, 1) # both chained transformations were 1-stable """ _type_ = AnyTransformation
[docs] def invoke(self, arg): """Execute a non-differentially-private query with `arg`. :param arg: Input to the transformation. :return: non-differentially-private answer :raises OpenDPException: packaged error from the core OpenDP library """ from opendp.core import transformation_invoke return transformation_invoke(self, arg)
def __call__(self, arg): from opendp.core import transformation_invoke return transformation_invoke(self, arg)
[docs] def check(self, d_in, d_out, *, debug=False): """Check if the transformation satisfies the stability relation at `d_in`, `d_out`. :param d_in: Distance in terms of the input metric. :param d_out: Distance in terms of the output metric. :param debug: Enable to raise Exceptions to help identify why the stability relation failed. :return: True if the relation passes. False if the relation failed. :rtype: bool :raises OpenDPException: packaged error from the core OpenDP library """ from opendp.core import transformation_check if debug: return transformation_check(self, d_in, d_out) try: return transformation_check(self, d_in, d_out) except OpenDPException as err: if err.variant == "RelationDebug": return False raise
def __rshift__(self, other: Union["Measurement", "Transformation"]): if isinstance(other, Measurement): from opendp.comb import make_chain_mt return make_chain_mt(other, self) if isinstance(other, Transformation): from opendp.comb import make_chain_tt return make_chain_tt(other, self) raise ValueError(f"rshift expected a measurement or transformation, got {other}") @property def input_distance_type(self): """Retrieve the distance type of the input metric. This may be any integral type for dataset metrics, or any numeric type for sensitivity metrics. :return: distance type """ from opendp.core import transformation_input_distance_type from opendp.typing import RuntimeType return RuntimeType.parse(transformation_input_distance_type(self)) @property def output_distance_type(self): """Retrieve the distance type of the output metric. This may be any integral type for dataset metrics, or any numeric type for sensitivity metrics. :return: distance type """ from opendp.core import transformation_output_distance_type from opendp.typing import RuntimeType return RuntimeType.parse(transformation_output_distance_type(self)) @property def input_carrier_type(self): """Retrieve the carrier type of the input domain. Any member of the input domain is a member of the carrier type. :return: carrier type """ from opendp.core import transformation_input_carrier_type from opendp.typing import RuntimeType return RuntimeType.parse(transformation_input_carrier_type(self)) def __del__(self): try: from opendp.core import _transformation_free _transformation_free(self) except ImportError: pass
[docs] class UnknownTypeException(Exception): pass
[docs] class OpenDPException(Exception): """General exception for errors originating from the underlying OpenDP library. The variant attribute corresponds to `one of the following variants <https://github.com/opendp/opendp/blob/53ec58d01762ca5ceee08590d7e7b725bbdafcf6/rust/opendp/src/error.rs#L46-L87>`_ and can be matched on. Error variants may change in library updates. .. todo:: Link to generated rust documentation for ErrorVariant. """ def __init__(self, variant: str, message: str = None, inner_traceback: str = None): self.variant = variant self.message = message self.inner_traceback = inner_traceback def __str__(self) -> str: response = self.variant if self.message: response += f'("{self.message}")' if self.inner_traceback: response += "\n" + '\n'.join('\t' + line for line in self.inner_traceback.split('\n')) return response
GLOBAL_FEATURES = set()
[docs] def enable_features(*features: str) -> None: GLOBAL_FEATURES.update(set(features))
[docs] def disable_features(*features: str) -> None: GLOBAL_FEATURES.difference_update(set(features))
[docs] def assert_features(*features: str) -> None: for feature in features: assert feature in GLOBAL_FEATURES, f"Attempted to use function that requires {feature}, but {feature} is not enabled. See https://github.com/opendp/opendp/discussions/304, then call enable_features(\"{feature}\")"
[docs] def binary_search_chain( make_chain: Callable[[Union[float, int]], Union[Transformation, Measurement]], d_in, d_out, bounds: Union[Tuple[float, float], Tuple[int, int]] = None, tolerance=None) -> Union[Transformation, Measurement]: """Optimizes a parameterized chain `make_chain` within float or integer `bounds`, subject to the chained relation being (`d_in`, `d_out`)-close. `bounds` defaults to (0., MAX_FINITE_FLOAT). If `bounds` are float, `tolerance` defaults to 1e-8. See `binary_search_param` to retrieve the discovered parameter instead of the complete computation chain. :param make_chain: a unary function that maps from a number to a Transformation or Measurement :param d_in: desired input distance of the computation chain :param d_out: desired output distance of the computation chain :param bounds: a 2-tuple of the lower and upper bounds to the input of `make_chain` :param tolerance: the discovered parameter differs by at most `tolerance` from the ideal parameter :return: a chain parameterized at the nearest passing value to the decision point of the relation :raises AssertionError: if the arguments are ill-formed (type issues, decision boundary not within `bounds`) :example: >>> from opendp.mod import binary_search_chain, enable_features >>> from opendp.trans import make_clamp, make_bounded_resize, make_sized_bounded_mean >>> from opendp.meas import make_base_laplace >>> enable_features("floating-point", "contrib") ... >>> # The majority of the chain only needs to be defined once. >>> pre = ( ... make_clamp(bounds=(0., 1.)) >> ... make_bounded_resize(size=10, bounds=(0., 1.), constant=0.) >> ... make_sized_bounded_mean(size=10, bounds=(0., 1.)) ... ) ... >>> # Find a value in `bounds` that produces a (`d_in`, `d_out`)-chain within `tolerance` of the decision boundary. >>> # The lambda function returns the complete computation chain when given a single numeric parameter. >>> chain = binary_search_chain(lambda s: pre >> make_base_laplace(scale=s), d_in=1, d_out=1.) ... >>> # The resulting computation chain is always (`d_in`, `d_out`)-close, but we can still double-check: >>> assert chain.check(1, 1.) """ return make_chain(binary_search_param(make_chain, d_in, d_out, bounds, tolerance))
[docs] def binary_search_param( make_chain: Callable[[Union[float, int]], Union[Transformation, Measurement]], d_in, d_out, bounds: Union[Tuple[float, float], Tuple[int, int]] = None, tolerance=None) -> Union[float, int]: """Optimizes a parameterized chain `make_chain` within float or integer `bounds`, subject to the chained relation being (`d_in`, `d_out`)-close. `bounds` defaults to (0., MAX_FINITE_FLOAT). If `bounds` are float, `tolerance` defaults to 1e-8. :example: >>> from opendp.mod import binary_search_param, enable_features >>> from opendp.meas import make_base_laplace ... >>> # Find a value in `bounds` that produces a (`d_in`, `d_out`)-chain within `tolerance` of the decision boundary. >>> # The first argument is any function that returns your complete computation chain >>> # when passed a single numeric parameter. >>> scale = binary_search_param(make_base_laplace, d_in=0.1, d_out=1.) >>> # The discovered scale differs by at most `tolerance` from the ideal scale (0.1). >>> assert scale - 0.1 < 1e-8 >>> # Constructing the same chain with the discovered parameter will always be (0.1, 1.)-close. >>> assert make_base_laplace(scale).check(0.1, 1.) :param make_chain: a unary function that maps from a number to a Transformation or Measurement :param d_in: desired input distance of the computation chain :param d_out: desired output distance of the computation chain :param bounds: a 2-tuple of the lower and upper bounds to the input of `make_chain` :param tolerance: the discovered parameter differs by at most `tolerance` from the ideal parameter :return: the nearest passing value to the decision point of the relation :raises AssertionError: if the arguments are ill-formed (type issues, decision boundary not within `bounds`) """ if bounds is None: import sys bounds = (0., sys.float_info.max) return binary_search(lambda param: make_chain(param).check(d_in, d_out), bounds, tolerance)