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// SPDX-License-Identifier: MPL-2.0
//! Differential privacy (DP) primitives.
//!
//! There are three main traits defined in this module:
//!
//! - `DifferentialPrivacyBudget`: Implementors should be types of DP-budgets,
//! i.e., methods to measure the amount of privacy provided by DP-mechanisms.
//! Examples: zCDP, ApproximateDP (Epsilon-Delta), PureDP
//!
//! - `DifferentialPrivacyDistribution`: Distribution from which noise is sampled.
//! Examples: DiscreteGaussian, DiscreteLaplace
//!
//! - `DifferentialPrivacyStrategy`: This is a combination of choices for budget and distribution.
//! Examples: zCDP-DiscreteGaussian, EpsilonDelta-DiscreteGaussian
//!
use num_bigint::{BigInt, BigUint, TryFromBigIntError};
use num_rational::{BigRational, Ratio};
use serde::{Deserialize, Serialize};
/// Errors propagated by methods in this module.
#[derive(Debug, thiserror::Error)]
#[non_exhaustive]
pub enum DpError {
/// Tried to use an invalid float as privacy parameter.
#[error(
"DP error: input value was not a valid privacy parameter. \
It should to be a non-negative, finite float."
)]
InvalidFloat,
/// Tried to construct a rational number with zero denominator.
#[error("DP error: input denominator was zero.")]
ZeroDenominator,
/// Tried to convert BigInt into something incompatible.
#[error("DP error: {0}")]
BigIntConversion(#[from] TryFromBigIntError<BigInt>),
}
/// Positive arbitrary precision rational number to represent DP and noise distribution parameters in
/// protocol messages and manipulate them without rounding errors.
#[derive(Clone, Debug)]
pub struct Rational(Ratio<BigUint>);
impl Rational {
/// Construct a [`Rational`] number from numerator `n` and denominator `d`. Errors if denominator is zero.
pub fn from_unsigned<T>(n: T, d: T) -> Result<Self, DpError>
where
T: Into<u128>,
{
// we don't want to expose BigUint in the public api, hence the Into<u128> bound
let d = d.into();
if d == 0 {
Err(DpError::ZeroDenominator)
} else {
Ok(Rational(Ratio::<BigUint>::new(n.into().into(), d.into())))
}
}
}
impl TryFrom<f32> for Rational {
type Error = DpError;
/// Constructs a `Rational` from a given `f32` value.
///
/// The special float values (NaN, positive and negative infinity) result in
/// an error. All other values are represented exactly, without rounding errors.
fn try_from(value: f32) -> Result<Self, DpError> {
match BigRational::from_float(value) {
Some(y) => Ok(Rational(Ratio::<BigUint>::new(
y.numer().clone().try_into()?,
y.denom().clone().try_into()?,
))),
None => Err(DpError::InvalidFloat)?,
}
}
}
/// Marker trait for differential privacy budgets (regardless of the specific accounting method).
pub trait DifferentialPrivacyBudget {}
/// Marker trait for differential privacy scalar noise distributions.
pub trait DifferentialPrivacyDistribution {}
/// Zero-concentrated differential privacy (ZCDP) budget as defined in [[BS16]].
///
#[derive(Clone, Debug, Eq, PartialEq, Serialize, Deserialize, Ord, PartialOrd)]
pub struct ZCdpBudget {
epsilon: Ratio<BigUint>,
}
impl ZCdpBudget {
/// Create a budget for parameter `epsilon`, using the notation from [[CKS20]] where `rho = (epsilon**2)/2`
/// for a `rho`-ZCDP budget.
///
pub fn new(epsilon: Rational) -> Self {
Self { epsilon: epsilon.0 }
}
}
impl DifferentialPrivacyBudget for ZCdpBudget {}
/// Strategy to make aggregate results differentially private, e.g. by adding noise from a specific
/// type of distribution instantiated with a given DP budget.
pub trait DifferentialPrivacyStrategy {
/// The type of the DP budget, i.e. the variant of differential privacy that can be obtained
/// by using this strategy.
type Budget: DifferentialPrivacyBudget;
/// The distribution type this strategy will use to generate the noise.
type Distribution: DifferentialPrivacyDistribution;
/// The type the sensitivity used for privacy analysis has.
type Sensitivity;
/// Create a strategy from a differential privacy budget. The distribution created with
/// `create_distribution` should provide the amount of privacy specified here.
fn from_budget(b: Self::Budget) -> Self;
/// Create a new distribution parametrized s.t. adding samples to the result of a function
/// with sensitivity `s` will yield differential privacy of the DP variant given in the
/// `Budget` type. Can error upon invalid parameters.
fn create_distribution(&self, s: Self::Sensitivity) -> Result<Self::Distribution, DpError>;
}
pub mod distributions;