Measurements#
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 mentioned in Getting Started, the intermediate domains and metrics need to match when chaining. This means you will need to choose a measurement that chains with your aggregator.
Additive Noise Mechanisms#
See the Additive Noise Mechanisms notebook for code examples and more exposition.
Notice that there is a symmetric structure to the additive noise measurements:
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In the following sections, 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]]
Laplacian Noise#
These algorithms accept sensitivities in terms of the absolute or L2 metrics and measure privacy in terms of epsilon.
Use the opendp.accuracy.laplacian_scale_to_accuracy() and opendp.accuracy.accuracy_to_laplacian_scale() functions to convert to/from accuracy estimates.
Measurement |
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Output Measure |
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There are more granular versions of these constructors, should you need them:
Expand Me
The primary constructors above switch between the cks20 and linear-time sampling algorithms depending on the noise scale. If the noise scale is greater than 10, the cks20 algorithm is more efficient. You can use these constructors to invoke the underlying algorithm directly. In addition, the linear-time algorithm supports a constant-time execution mode if noise bounds are passed.
Measurement |
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Input Metric |
Output Measure |
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Gaussian Noise#
These algorithms accept sensitivities in terms of the absolute or L2 metrics and measure privacy in terms of rho (zero-concentrated differential privacy).
Use the opendp.accuracy.gaussian_scale_to_accuracy() and opendp.accuracy.accuracy_to_gaussian_scale() functions to convert to/from accuracy estimates.
Refer to Measure Casting to convert to approximate DP.
Measurement |
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Output Measure |
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Geometric Noise#
The geometric mechanism (make_base_geometric) has been deprecated in favor of the discrete laplace (make_base_discrete_laplace).
make_base_discrete_laplace is overall more computationally efficient than the previous algorithm.
If you need constant-time execution to protect against timing side-channels, use opendp.measurements.make_base_discrete_laplace_linear(), which is equivalent to the previous algorithm.
Stability Histogram#
The stability histogram is used to release a category set and frequency counts, and is useful when the category set is unknown or very large. make_count_by is included here because it is currently the only transformation that make_base_ptr chains with.
See the Histograms notebook for code examples and more exposition.
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Input Metric |
Output Metric/Measure |
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Randomized Response#
These measurements are used to randomize an individual’s response to a query in the local-DP model.
See the Randomized Response notebook for code examples and more exposition.
Measurement |
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Output Measure |
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