opendp.meas module

opendp.meas.make_base_gaussian(scale, D='AllDomain<T>')

Make a Measurement that adds noise from the gaussian(scale) distribution to the input. Adjust D to noise vector-valued data.

Parameters:

• scale – noise scale parameter to the gaussian distribution

• D (RuntimeTypeDescriptor) – Domain of the data type to be privatized. Valid values are VectorDomain<AllDomain<T>> or AllDomain<T>

Returns:

A base_gaussian step.

Return type:

Measurement

Raises:

• AssertionError – 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.meas.make_base_geometric(scale, bounds=None, D='AllDomain<i32>', QO=None)

Make a Measurement that adds noise from the geometric(scale) distribution to the input. Adjust D to noise vector-valued data.

Parameters:

• scale – noise scale parameter to the geometric distribution

• bounds (Any) – Set bounds on the count to make the algorithm run in constant-time.

• D (RuntimeTypeDescriptor) – Domain of the data type to be privatized. Valid values are VectorDomain<AllDomain<T>> or AllDomain<T>

• QO (RuntimeTypeDescriptor) – Data type of the sensitivity, scale, and budget.

Returns:

A base_geometric step.

Return type:

Measurement

Raises:

• AssertionError – 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.meas.make_base_laplace(scale, D='AllDomain<T>')

Make a Measurement that adds noise from the laplace(scale) distribution to a scalar value. Adjust D to noise vector-valued data.

Parameters:

• scale – Noise scale parameter of the laplace distribution.

• D (RuntimeTypeDescriptor) – Domain of the data type to be privatized. Valid values are VectorDomain<AllDomain<T>> or AllDomain<T>

Returns:

A base_laplace step.

Return type:

Measurement

Raises:

• AssertionError – 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.meas.make_base_stability(size, scale, threshold, MI, TIK, TIC='i32')

Make a Measurement that implements a stability-based filtering and noising.

Parameters:

• size (int) – Number of records in the input vector.

• scale – Noise scale parameter.

• threshold – Exclude counts that are less than this minimum value.

• MI (SensitivityMetric) – Input metric.

• TIK (RuntimeTypeDescriptor) – Data type of input key- must be hashable/categorical.

• TIC (RuntimeTypeDescriptor) – Data type of input count- must be integral.

Returns:

A base_stability step.

Return type:

Measurement

Raises:

• AssertionError – 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