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//! A library for working with differential privacy. //! //! This library implements the framework described in the paper, //! [A Programming Framework for OpenDP](https://projects.iq.harvard.edu/files/opendp/files/opendp_programming_framework_11may2020_1_01.pdf). //! OpenDP (the library) is part of the larger [OpenDP Project](https://opendp.org). //! //! [`Domain`]: core::Domain //! [`Domain::Carrier`]: core::Domain::Carrier //! [`Function`]: core::Function //! [`Metric`]: core::Metric //! [`Measure`]: core::Measure //! [`PrivacyRelation`]: core::PrivacyRelation //! [`StabilityRelation`]: core::StabilityRelation //! [`Measurement`]: core::Measurement //! [`Transformation`]: core::Transformation //! //! # Overview //! //! OpenDP provides three main concepts: //! * A flexible architecture for modeling privacy-preserving computations. //! * Implementations of several common algorithms for statistical analysis and data manipulation, which can be used //! out-of-the-box to assemble DP applications. //! * Facilities for extending OpenDP with new algorithms, privacy models, etc. //! //! In addition, there's a companion crate, opendp-ffi, which provides FFI wrappers for opendp functionality. //! This can be used to implement bindings in languages other than Rust. //! //! # User Guide //! //! OpenDP applications are created by using constructors and combinators to create private computation pipelines. //! These can be written directly in Rust, or by using a language binding that uses OpenDP through an FFI interface. //! Python is the first language binding available, but we made add others in the future. //! //! ## Rust Application Example //! //! Here's a simple example of using OpenDP from Rust to create a private sum: //! ``` //! use opendp::core; //! use opendp::meas; //! use opendp::trans; //! use opendp::trans::{manipulation, sum, make_split_lines, make_cast_default, make_clamp, make_bounded_sum}; //! use opendp::dist::{SubstituteDistance, L1Distance}; //! use opendp::error::*; //! use opendp::chain::{make_chain_tt, make_chain_mt}; //! use opendp::meas::make_base_laplace; //! use opendp::dom::VectorDomain; //! //! pub fn example() -> Fallible<()> { //! let data = "56\n15\n97\n56\n6\n17\n2\n19\n16\n50".to_owned(); //! let bounds = (0.0, 100.0); //! let epsilon = 1.0; //! let sigma = (bounds.1 - bounds.0) / epsilon; //! //! // Construct a Transformation to load the numbers. //! let split_lines = make_split_lines()?; //! let cast = make_cast_default::<String, f64>()?; //! let load_numbers = make_chain_tt(&cast, &split_lines, None)?; //! //! // Construct a Measurement to calculate a noisy sum. //! let clamp = make_clamp::<VectorDomain<_>, _>(bounds.0, bounds.1)?; //! let bounded_sum = make_bounded_sum(bounds.0, bounds.1)?; //! let laplace = make_base_laplace(sigma)?; //! let intermediate = make_chain_tt(&bounded_sum, &clamp, None)?; //! let noisy_sum = make_chain_mt(&laplace, &intermediate, None)?; //! //! // Put it all together. //! let pipeline = make_chain_mt(&noisy_sum, &load_numbers, None)?; //! let result = pipeline.function.eval(&data)?; //! println!("result = {}", result); //! Ok(()) //! } //! example().unwrap_test(); //! ``` //! //! # Contributor Guide //! //! Contributions to OpenDP typically take the form of what we call "Components." A Component is shorthand for //! the collection of code that comprises a [`Measurement`] or [`Transformation`]. //! //! ## Adding Components //! //! OpenDP components take the form of constructor functions that construct new instances of [`Measurement`] //! and [`Transformation`]. Measurement constructors go in the module [`meas`], and Transformation constructors //! in the module [`trans`]. (We'll probably split these up as they grow.) //! //! There are two steps to adding a constructor function: Writing the function itself, and adding the FFI wrapper. //! //! ### Writing Constructors //! //! Constructors are functions that take configuration parameters and return an appropriately configured [`Measurement`] or [`Transformation`]. //! They typically follow a common pattern: //! 1. Choose the appropriate input and output [`Domain`]. //! 2. Write a closure that implements the [`Function`]. //! 3. Choose the appropriate input and output [`Metric`]/[`Measure`]. //! 4. Write a closure that implements the [`PrivacyRelation`]/[`StabilityRelation`]. //! //! #### Example Transformation Constructor //! ``` //!# use opendp::core::{Transformation, StabilityRelation, Function}; //!# use opendp::dist::L1Distance; //!# use opendp::dom::AllDomain; //! pub fn make_i32_identity() -> Transformation<AllDomain<i32>, AllDomain<i32>, L1Distance<i32>, L1Distance<i32>> { //! let input_domain = AllDomain::new(); //! let output_domain = AllDomain::new(); //! let function = Function::new(|arg: &i32| -> i32 { *arg }); //! let input_metric = L1Distance::default(); //! let output_metric = L1Distance::default(); //! let stability_relation = StabilityRelation::new_from_constant(1); //! Transformation::new(input_domain, output_domain, function, input_metric, output_metric, stability_relation) //! } //! make_i32_identity(); //! ``` //! //! #### Input and Output Types //! //! The [`Function`] created in a constructor is allowed to have any type for its input and output [`Domain::Carrier`]. //! There's no need for special data carrying wrappers. The clue code in the FFI layer handles this transparently. //! However, the most common are the Rust primitives (e.g., `i32`, `f64`, etc.), and collections of the primitives //! (`Vec<i32>`, `HashMap<String, f64>`). //! //! //! #### Handling Generics //! //! [`Measurement`]/[`Transformation`] constructors are allowed to be generic! Typically, this means that the type parameter on the //! constructor will determine type of the input or output [`Domain::Carrier`] (or the generic type within, for instance the `i32` of `Vec<i32>`). #![allow(clippy::upper_case_acronyms)] #![allow(clippy::just_underscores_and_digits)] #![allow(clippy::type_complexity)] // create clones of variables that are free to be consumed by a closure macro_rules! enclose { ( $x:ident, $y:expr ) => (enclose!(($x), $y)); ( ($( $x:ident ),*), $y:expr ) => { { $(let $x = $x.clone();)* $y } }; } #[macro_use] pub mod error; pub mod chain; pub mod core; pub mod data; pub mod dist; pub mod dom; pub mod interactive; pub mod meas; pub mod poly; pub mod samplers; pub mod traits; pub mod trans;