Measurement Constructors#
This section gives a high-level overview of the measurements that are available in the library. Refer to the Measurement section for an explanation of what a measurement is.
As covered in the Chaining section, the intermediate domains and metrics need to match when chaining. This means you will need to choose a measurement that chains with your aggregator.
In the following table, the scalar-valued and vector-valued versions of each measurement are listed separately.
You can choose whether to construct scalar or vector-valued versions by setting the D type argument when calling the constructor.
- Scalar:
D=AllDomain[T](default)- Vector:
D=VectorDomain[AllDomain[T]]
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Input Domain |
Output Metric |
Output Measure |
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Floating-Point#
Given the context of measurements, this section goes into greater detail than Limitations on floating-point issues.
Be warned that opendp.meas.make_base_laplace(), opendp.meas.make_base_gaussian() and opendp.meas.make_base_stability()
depend on continuous distributions that are poorly approximated by finite computers.
At this time these mechanisms are present in the library, but require explicit opt-in:
>>> from opendp.mod import enable_features
>>> enable_features("floating-point")
The canonical paper on this and introduction of the snapping mechanism is here: On Significance of the Least Significant Bits For Differential Privacy.
Precautions have been made to sample noise using the MPFR library, to avoid artifacts in noise, but our noise postprocessing re-introduces artifacts. We are developing alternative mechanisms for answering continuous queries.
We acknowledge the snapping mechanism and have an implementation of it in PR #84.
We are also working towards adding support for fixed-point data types in PR #184.