opendp 0.14.2-dev.20260401.2

A library of differential privacy algorithms for the statistical analysis of sensitive private data.
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use crate::{
    error::Fallible,
    measures::{MaxDivergence, ZeroConcentratedDivergence},
};

use super::*;

fn check_rnm_outcome<M: TopKMeasure>(
    measure: M,
    scale: f64,
    negate: bool,
    input: Vec<i32>,
    expected_idx: usize,
    expected_loss: f64,
) -> Fallible<()> {
    let m_rnm = make_noisy_max(
        VectorDomain::new(AtomDomain::default()),
        LInfDistance::default(),
        measure,
        scale,
        negate,
    )?;
    assert_eq!(m_rnm.invoke(&input)?, expected_idx);
    assert_eq!(m_rnm.map(&1)?, expected_loss);
    Ok(())
}

#[test]
fn test_max_vs_min_gumbel() -> Fallible<()> {
    check_rnm_outcome(
        ZeroConcentratedDivergence,
        0.,
        false,
        vec![1, 2, 3],
        2,
        f64::INFINITY,
    )?;
    check_rnm_outcome(
        ZeroConcentratedDivergence,
        0.,
        true,
        vec![1, 2, 3],
        0,
        f64::INFINITY,
    )?;
    check_rnm_outcome(
        ZeroConcentratedDivergence,
        1.,
        false,
        vec![1, 1, 100_000],
        2,
        0.5,
    )?;
    check_rnm_outcome(
        ZeroConcentratedDivergence,
        1.,
        true,
        vec![1, 100_000, 100_000],
        0,
        0.5,
    )?;
    Ok(())
}

#[test]
fn test_max_vs_min_exponential() -> Fallible<()> {
    check_rnm_outcome(MaxDivergence, 0., false, vec![1, 2, 3], 2, f64::INFINITY)?;
    check_rnm_outcome(MaxDivergence, 0., true, vec![1, 2, 3], 0, f64::INFINITY)?;
    check_rnm_outcome(MaxDivergence, 1., false, vec![1, 1, 100_000], 2, 2.0)?;
    check_rnm_outcome(MaxDivergence, 1., true, vec![1, 100_000, 100_000], 0, 2.0)?;
    Ok(())
}