Function reference • R Documentation

Accuracy

The accuracy module provides functions for converting between accuracy and scale parameters.

accuracy_to_discrete_gaussian_scale(): Convert a desired accuracy (tolerance) into a discrete gaussian noise scale at a statistical significance level alpha.

accuracy_to_discrete_laplacian_scale(): Convert a desired accuracy (tolerance) into a discrete Laplacian noise scale at a statistical significance level alpha.

accuracy_to_gaussian_scale(): Convert a desired accuracy (tolerance) into a gaussian noise scale at a statistical significance level alpha.

accuracy_to_laplacian_scale(): Convert a desired accuracy (tolerance) into a Laplacian noise scale at a statistical significance level alpha.

discrete_gaussian_scale_to_accuracy(): Convert a discrete gaussian scale into an accuracy estimate (tolerance) at a statistical significance level alpha.

discrete_laplacian_scale_to_accuracy(): Convert a discrete Laplacian scale into an accuracy estimate (tolerance) at a statistical significance level alpha.

gaussian_scale_to_accuracy(): Convert a gaussian scale into an accuracy estimate (tolerance) at a statistical significance level alpha.

laplacian_scale_to_accuracy(): Convert a Laplacian scale into an accuracy estimate (tolerance) at a statistical significance level alpha.

Combinators

The combinators module provides functions for combining transformations and measurements.

make_basic_composition(): basic composition constructor

make_chain_mt(): chain mt constructor

make_chain_pm(): chain pm constructor

make_chain_tt(): chain tt constructor

make_fix_delta(): fix delta constructor

make_population_amplification(): population amplification constructor

make_pureDP_to_fixed_approxDP(): pureDP to fixed approxDP constructor

make_pureDP_to_zCDP(): pureDP to zCDP constructor

make_sequential_composition(): sequential composition constructor

make_zCDP_to_approxDP(): zCDP to approxDP constructor

then_basic_composition(): partial basic composition constructor

then_chain_mt(): partial chain mt constructor

then_chain_pm(): partial chain pm constructor

then_chain_tt(): partial chain tt constructor

then_fix_delta(): partial fix delta constructor

then_population_amplification(): partial population amplification constructor

then_pureDP_to_fixed_approxDP(): partial pureDP to fixed approxDP constructor

then_pureDP_to_zCDP(): partial pureDP to zCDP constructor

then_sequential_composition(): partial sequential composition constructor

then_zCDP_to_approxDP(): partial zCDP to approxDP constructor

Core

The core module provides functions for accessing the fields of transformations and measurements.

function_eval(): Eval the function with arg.

measurement_check(): Check the privacy relation of the measurement at the given d_in, d_out

measurement_function(): Get the function from a measurement.

measurement_input_carrier_type(): Get the input (carrier) data type of this.

measurement_input_distance_type(): Get the input distance type of measurement.

measurement_input_domain(): Get the input domain from a measurement.

measurement_input_metric(): Get the input domain from a measurement.

measurement_invoke(): Invoke the measurement with arg. Returns a differentially private release.

measurement_map(): Use the measurement to map a given d_in to d_out.

measurement_output_distance_type(): Get the output distance type of measurement.

measurement_output_measure(): Get the output domain from a measurement.

queryable_eval(): Invoke the queryable with query. Returns a differentially private release.

queryable_query_type(): Get the query type of queryable.

transformation_check(): Check the privacy relation of the measurement at the given d_in, d_out

transformation_function(): Get the function from a transformation.

transformation_input_carrier_type(): Get the input (carrier) data type of this.

transformation_input_distance_type(): Get the input distance type of transformation.

transformation_input_domain(): Get the input domain from a transformation.

transformation_input_metric(): Get the input domain from a transformation.

transformation_invoke(): Invoke the transformation with arg. Returns a differentially private release.

transformation_map(): Use the transformation to map a given d_in to d_out.

transformation_output_distance_type(): Get the output distance type of transformation.

transformation_output_domain(): Get the output domain from a transformation.

transformation_output_metric(): Get the output domain from a transformation.

Domains

The domains module provides functions for creating and using domains.

atom_domain(): Construct an instance of AtomDomain.

domain_carrier_type(): Get the carrier type of a domain.

domain_debug(): Debug a domain.

domain_type(): Get the type of a domain.

map_domain(): Construct an instance of MapDomain.

member(): Check membership in a domain.

option_domain(): Construct an instance of OptionDomain.

vector_domain(): Construct an instance of VectorDomain.

Measurements

The measurements module provides functions that apply calibrated noise to data to ensure differential privacy.

make_alp_queryable(): alp queryable constructor

make_base_laplace_threshold(): base laplace threshold constructor

make_gaussian(): gaussian constructor

make_geometric(): geometric constructor

make_laplace(): laplace constructor

make_randomized_response(): randomized response constructor

make_randomized_response_bool(): randomized response bool constructor

make_report_noisy_max_gumbel(): report noisy max gumbel constructor

then_alp_queryable(): partial alp queryable constructor

then_base_laplace_threshold(): partial base laplace threshold constructor

then_gaussian(): partial gaussian constructor

then_geometric(): partial geometric constructor

then_laplace(): partial laplace constructor

then_randomized_response(): partial randomized response constructor

then_randomized_response_bool(): partial randomized response bool constructor

then_report_noisy_max_gumbel(): partial report noisy max gumbel constructor

Measures

The measures modules provides functions that measure the distance between probability distributions.

fixed_smoothed_max_divergence(): Construct an instance of the FixedSmoothedMaxDivergence measure.

max_divergence(): Construct an instance of the MaxDivergence measure.

measure_debug(): Debug a measure.

measure_distance_type(): Get the distance type of a measure.

measure_type(): Get the type of a measure.

smoothed_max_divergence(): Construct an instance of the SmoothedMaxDivergence measure.

user_divergence(): Construct a new UserDivergence. Any two instances of an UserDivergence are equal if their string descriptors are equal.

zero_concentrated_divergence(): Construct an instance of the ZeroConcentratedDivergence measure.

Metrics

The metrics module provides fuctions that measure the distance between two elements of a domain.

absolute_distance(): Construct an instance of the AbsoluteDistance metric.

change_one_distance(): Construct an instance of the ChangeOneDistance metric.

discrete_distance(): Construct an instance of the DiscreteDistance metric.

hamming_distance(): Construct an instance of the HammingDistance metric.

insert_delete_distance(): Construct an instance of the InsertDeleteDistance metric.

l1_distance(): Construct an instance of the L1Distance metric.

l2_distance(): Construct an instance of the L2Distance metric.

linf_distance(): Construct an instance of the LInfDistance metric.

metric_debug(): Debug a metric.

metric_distance_type(): Get the distance type of a metric.

metric_type(): Get the type of a metric.

symmetric_distance(): Construct an instance of the SymmetricDistance metric.

user_distance(): Construct a new UserDistance. Any two instances of an UserDistance are equal if their string descriptors are equal.

Mod

The mod module provides the classes which implement the OpenDP Programming Framework, as well as utilities for enabling features and finding parameter values.

binary_search(): Find the closest passing value to the decision boundary of predicate

binary_search_chain(): Find the highest-utility (d_in, d_out)-close Transformation or Measurement.

binary_search_param(): Solve for the ideal constructor argument to make_chain

disable_features(): Disable features in the opendp package.

enable_features(): Enable features for the opendp package.

hashitems(): extract heterogeneously typed keys and values from a hashtab

new_domain(): new domain

new_function(): new function

new_hashtab(): create an instance of a hashtab from keys and values

new_measure(): new measure

new_measurement(): new measurement

new_metric(): new metric

new_queryable(): new queryable

new_smd_curve(): new Smoothed Max Divergence curve

new_transformation(): new transformation

Transformations

The transformations module provides functions that deterministicly transform datasets.

choose_branching_factor(): Returns an approximation to the ideal branching_factor for a dataset of a given size, that minimizes error in cdf and quantile estimates based on b-ary trees.

make_b_ary_tree(): b ary tree constructor

make_bounded_float_checked_sum(): bounded float checked sum constructor

make_bounded_float_ordered_sum(): bounded float ordered sum constructor

make_bounded_int_monotonic_sum(): bounded int monotonic sum constructor

make_bounded_int_ordered_sum(): bounded int ordered sum constructor

make_bounded_int_split_sum(): bounded int split sum constructor

make_cast(): cast constructor

make_cast_default(): cast default constructor

make_cast_inherent(): cast inherent constructor

make_cdf(): cdf constructor

make_clamp(): clamp constructor

make_consistent_b_ary_tree(): consistent b ary tree constructor

make_count(): count constructor

make_count_by(): count by constructor

make_count_by_categories(): count by categories constructor

make_count_distinct(): count distinct constructor

make_create_dataframe(): create dataframe constructor

make_df_cast_default(): df cast default constructor

make_df_is_equal(): df is equal constructor

make_drop_null(): drop null constructor

make_find(): find constructor

make_find_bin(): find bin constructor

make_identity(): identity constructor

make_impute_constant(): impute constant constructor

make_impute_uniform_float(): impute uniform float constructor

make_index(): index constructor

make_is_equal(): is equal constructor

make_is_null(): is null constructor

make_lipschitz_float_mul(): lipschitz float mul constructor

make_mean(): mean constructor

make_metric_bounded(): metric bounded constructor

make_metric_unbounded(): metric unbounded constructor

make_ordered_random(): ordered random constructor

make_quantile_score_candidates(): quantile score candidates constructor

make_quantiles_from_counts(): quantiles from counts constructor

make_resize(): resize constructor

make_select_column(): select column constructor

make_sized_bounded_float_checked_sum(): sized bounded float checked sum constructor

make_sized_bounded_float_ordered_sum(): sized bounded float ordered sum constructor

make_sized_bounded_int_checked_sum(): sized bounded int checked sum constructor

make_sized_bounded_int_monotonic_sum(): sized bounded int monotonic sum constructor

make_sized_bounded_int_ordered_sum(): sized bounded int ordered sum constructor

make_sized_bounded_int_split_sum(): sized bounded int split sum constructor

make_split_dataframe(): split dataframe constructor

make_split_lines(): split lines constructor

make_split_records(): split records constructor

make_subset_by(): subset by constructor

make_sum(): sum constructor

make_sum_of_squared_deviations(): sum of squared deviations constructor

make_unordered(): unordered constructor

make_variance(): variance constructor

then_b_ary_tree(): partial b ary tree constructor

then_bounded_float_checked_sum(): partial bounded float checked sum constructor

then_bounded_float_ordered_sum(): partial bounded float ordered sum constructor

then_bounded_int_monotonic_sum(): partial bounded int monotonic sum constructor

then_bounded_int_ordered_sum(): partial bounded int ordered sum constructor

then_bounded_int_split_sum(): partial bounded int split sum constructor

then_cast(): partial cast constructor

then_cast_default(): partial cast default constructor

then_cast_inherent(): partial cast inherent constructor

then_cdf(): partial cdf constructor

then_clamp(): partial clamp constructor

then_consistent_b_ary_tree(): partial consistent b ary tree constructor

then_count(): partial count constructor

then_count_by(): partial count by constructor

then_count_by_categories(): partial count by categories constructor

then_count_distinct(): partial count distinct constructor

then_create_dataframe(): partial create dataframe constructor

then_df_cast_default(): partial df cast default constructor

then_df_is_equal(): partial df is equal constructor

then_drop_null(): partial drop null constructor

then_find(): partial find constructor

then_find_bin(): partial find bin constructor

then_identity(): partial identity constructor

then_impute_constant(): partial impute constant constructor

then_impute_uniform_float(): partial impute uniform float constructor

then_index(): partial index constructor

then_is_equal(): partial is equal constructor

then_is_null(): partial is null constructor

then_lipschitz_float_mul(): partial lipschitz float mul constructor

then_mean(): partial mean constructor

then_metric_bounded(): partial metric bounded constructor

then_metric_unbounded(): partial metric unbounded constructor

then_ordered_random(): partial ordered random constructor

then_quantile_score_candidates(): partial quantile score candidates constructor

then_quantiles_from_counts(): partial quantiles from counts constructor

then_resize(): partial resize constructor

then_select_column(): partial select column constructor

then_sized_bounded_float_checked_sum(): partial sized bounded float checked sum constructor

then_sized_bounded_float_ordered_sum(): partial sized bounded float ordered sum constructor

then_sized_bounded_int_checked_sum(): partial sized bounded int checked sum constructor

then_sized_bounded_int_monotonic_sum(): partial sized bounded int monotonic sum constructor

then_sized_bounded_int_ordered_sum(): partial sized bounded int ordered sum constructor

then_sized_bounded_int_split_sum(): partial sized bounded int split sum constructor

then_split_dataframe(): partial split dataframe constructor

then_split_lines(): partial split lines constructor

then_split_records(): partial split records constructor

then_subset_by(): partial subset by constructor

then_sum(): partial sum constructor

then_sum_of_squared_deviations(): partial sum of squared deviations constructor

then_unordered(): partial unordered constructor

then_variance(): partial variance constructor

Typing

The typing module provides utilities that bridge between R and Rust types. OpenDP relies on precise descriptions of data types to make its security guarantees: These are more natural in Rust with its fine-grained type system, but they may feel out of place in R. These utilities try to fill that gap.

String: type signature for a string

bool: type signature for a boolean

f32: type signature for a 32-bit floating point number

f64: type signature for a 64-bit floating point number

i128: type signature for a 128-bit signed integer

i16: type signature for a 16-bit signed integer

i32: type signature for a 32-bit signed integer

i64: type signature for a 64-bit signed integer

i8: type signature for an 8-bit signed integer

u128: type signature for a 128-bit unsigned integer

u16: type signature for a 16-bit unsigned integer

u32: type signature for a 32-bit unsigned integer

u64: type signature for a 64-bit unsigned integer

u8: type signature for an 8-bit unsigned integer

usize: type signature for a pointer-sized unsigned integer

Other

This should be empty if correctly configured. Please file an issue if any functions are listed here.