This documentation is for an old version of OpenDP.

The current release of OpenDP is v0.11.1.

opendp.combinators module#

The combinators module provides functions for combining transformations and measurements.

opendp.combinators.make_basic_composition(measurements)[source]#

Construct the DP composition [measurement0, measurement1, …]. Returns a Measurement that when invoked, computes [measurement0(x), measurement1(x), ...]

All metrics and domains must be equivalent.

Composition Properties

sequential: all measurements are applied to the same dataset
basic: the composition is the linear sum of the privacy usage of each query
noninteractive: all mechanisms specified up-front (but each can be interactive)
compositor: all privacy parameters specified up-front (via the map)

make_basic_composition in Rust documentation.

Parameters:

measurements (Any) – A vector of Measurements to compose.

Return type:

Measurement

Raises:

TypeError – if an argument’s type differs from the expected type
UnknownTypeException – if a type argument fails to parse
OpenDPException – packaged error from the core OpenDP library

opendp.combinators.make_chain_mt(measurement1, transformation0)[source]#

Construct the functional composition (measurement1 ○ transformation0). Returns a Measurement that when invoked, computes measurement1(transformation0(x)).

make_chain_mt in Rust documentation.

Parameters:

measurement1 (Measurement) – outer mechanism
transformation0 (Transformation) – inner transformation

Return type:

Measurement

Raises:

TypeError – if an argument’s type differs from the expected type
UnknownTypeException – if a type argument fails to parse
OpenDPException – packaged error from the core OpenDP library

opendp.combinators.make_chain_pm(postprocess1, measurement0)[source]#

Construct the functional composition (postprocess1 ○ measurement0). Returns a Measurement that when invoked, computes postprocess1(measurement0(x)). Used to represent non-interactive postprocessing.

make_chain_pm in Rust documentation.

Parameters:

postprocess1 (Function) – outer postprocessor
measurement0 (Measurement) – inner measurement/mechanism

Return type:

Measurement

Raises:

TypeError – if an argument’s type differs from the expected type
UnknownTypeException – if a type argument fails to parse
OpenDPException – packaged error from the core OpenDP library

opendp.combinators.make_chain_tt(transformation1, transformation0)[source]#

Construct the functional composition (transformation1 ○ transformation0). Returns a Transformation that when invoked, computes transformation1(transformation0(x)).

make_chain_tt in Rust documentation.

Parameters:

transformation1 (Transformation) – outer transformation
transformation0 (Transformation) – inner transformation

Return type:

Transformation

Raises:

TypeError – if an argument’s type differs from the expected type
UnknownTypeException – if a type argument fails to parse
OpenDPException – packaged error from the core OpenDP library

opendp.combinators.make_fix_delta(measurement, delta)[source]#

Fix the delta parameter in the privacy map of a measurement with a SmoothedMaxDivergence output measure.

make_fix_delta in Rust documentation.

Parameters:

measurement (Measurement) – a measurement with a privacy curve to be fixed
delta (Any) – parameter to fix the privacy curve with

Return type:

Measurement

Raises:

TypeError – if an argument’s type differs from the expected type
UnknownTypeException – if a type argument fails to parse
OpenDPException – packaged error from the core OpenDP library

opendp.combinators.make_population_amplification(measurement, population_size)[source]#

Construct an amplified measurement from a measurement with privacy amplification by subsampling. This measurement does not perform any sampling. It is useful when you have a dataset on-hand that is a simple random sample from a larger population.

The DIA, DO, MI and MO between the input measurement and amplified output measurement all match.

Protected by the “honest-but-curious” feature flag because a dishonest adversary could set the population size to be arbitrarily large.

make_population_amplification in Rust documentation.

Parameters:

measurement (Measurement) – the computation to amplify
population_size (int) – the size of the population from which the input dataset is a simple sample

Return type:

Measurement

Raises:

TypeError – if an argument’s type differs from the expected type
UnknownTypeException – if a type argument fails to parse
OpenDPException – packaged error from the core OpenDP library

opendp.combinators.make_pureDP_to_fixed_approxDP(measurement)[source]#

Constructs a new output measurement where the output measure is casted from MaxDivergence<QO> to FixedSmoothedMaxDivergence<QO>.

make_pureDP_to_fixed_approxDP in Rust documentation.

Parameters:

measurement (Measurement) – a measurement with a privacy measure to be casted

Return type:

Measurement

Raises:

TypeError – if an argument’s type differs from the expected type
UnknownTypeException – if a type argument fails to parse
OpenDPException – packaged error from the core OpenDP library

opendp.combinators.make_pureDP_to_zCDP(measurement)[source]#

Constructs a new output measurement where the output measure is casted from MaxDivergence<QO> to ZeroConcentratedDivergence<QO>.

make_pureDP_to_zCDP in Rust documentation.

Citations:

BS16 Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds

Parameters:

measurement (Measurement) – a measurement with a privacy measure to be casted

Return type:

Measurement

Raises:

TypeError – if an argument’s type differs from the expected type
UnknownTypeException – if a type argument fails to parse
OpenDPException – packaged error from the core OpenDP library

opendp.combinators.make_sequential_composition(input_domain, input_metric, output_measure, d_in, d_mids)[source]#

Construct a Measurement that when invoked, returns a queryable that interactively composes measurements.

Composition Properties

sequential: all measurements are applied to the same dataset
basic: the composition is the linear sum of the privacy usage of each query
interactive: mechanisms can be specified based on answers to previous queries
compositor: all privacy parameters specified up-front

If the privacy measure supports concurrency, this compositor allows you to spawn multiple interactive mechanisms and interleave your queries amongst them.

make_sequential_composition in Rust documentation.

Supporting Elements:

Input Domain: DI
Output Type: Queryable<Measurement<DI, TO, MI, MO>, TO>
Input Metric: MI
Output Measure: MO

Parameters:

input_domain (Domain) – indicates the space of valid input datasets
input_metric (Metric) – how distances are measured between members of the input domain
output_measure (Measure) – how privacy is measured
d_in (Any) – maximum distance between adjacent input datasets
d_mids (Any) – maximum privacy expenditure of each query

Return type:

Measurement

Raises:

TypeError – if an argument’s type differs from the expected type
UnknownTypeException – if a type argument fails to parse
OpenDPException – packaged error from the core OpenDP library

opendp.combinators.make_zCDP_to_approxDP(measurement)[source]#

Constructs a new output measurement where the output measure is casted from ZeroConcentratedDivergence<QO> to SmoothedMaxDivergence<QO>.

make_zCDP_to_approxDP in Rust documentation.

Parameters:

measurement (Measurement) – a measurement with a privacy measure to be casted

Return type:

Measurement

Raises:

TypeError – if an argument’s type differs from the expected type
UnknownTypeException – if a type argument fails to parse
OpenDPException – packaged error from the core OpenDP library

opendp.combinators.then_sequential_composition(output_measure, d_in, d_mids)[source]#

partial constructor of make_sequential_composition

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opendp.combinators module#