Struct Differential Privacy &nbsp; Copy item path

impl DifferentialPrivacy

pub fn new() -> Self

pub fn with_budget( epsilon: f64, delta: f64, composition_method: CompositionMethod, ) -> Result<Self, PrivacyError>

pub fn validate(&self) -> Result<(), PrivacyError>

pub fn release_laplace_into( &mut self, query_result: &[f64], l1_sensitivity: f64, epsilon: f64, out: &mut [f64], ) -> Result<usize, PrivacyError>

Release with Laplace noise drawn from operating-system cryptographic entropy.

pub fn release_laplace_with_noise_into<R: NoiseSource>( &mut self, query_result: &[f64], l1_sensitivity: f64, epsilon: f64, noise: &mut R, out: &mut [f64], ) -> Result<usize, PrivacyError>

Release a vector-valued query with Laplace noise calibrated to L1 sensitivity.

Custom sources are useful for deterministic tests. A source that is not a CSPRNG invalidates the differential-privacy guarantee.

pub fn release_gaussian_into( &mut self, query_result: &[f64], l2_sensitivity: f64, epsilon: f64, delta: f64, out: &mut [f64], ) -> Result<usize, PrivacyError>

Release with Gaussian noise drawn from operating-system cryptographic entropy.

pub fn release_gaussian_with_noise_into<R: NoiseSource>( &mut self, query_result: &[f64], l2_sensitivity: f64, epsilon: f64, delta: f64, noise: &mut R, out: &mut [f64], ) -> Result<usize, PrivacyError>

Release a vector-valued query with the classic calibrated Gaussian mechanism.

This sufficient calibration is valid for 0 < epsilon <= 1 and 0 < delta < 1: σ = sensitivity * sqrt(2 ln(1.25 / δ)) / ε.

Trait Implementations§

impl Default for DifferentialPrivacy

fn default() -> Self

Returns the “default value” for a type. Read more

Auto Trait Implementations§

impl UnwindSafe for DifferentialPrivacy

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