opendp 0.14.2-dev.20260401.2

A library of differential privacy algorithms for the statistical analysis of sensitive private data.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45

use dashu::rbig;

use crate::metrics::{L1Distance, L2Distance};

use super::*;

#[test]
fn test_make_noise_ibig_laplace() -> Fallible<()> {
    let space = (
        VectorDomain::new(AtomDomain::<IBig>::default()),
        L1Distance::<RBig>::default(),
    );

    assert!(
        ZExpFamily::<1> { scale: rbig!(-1) }
            .make_noise(space.clone())
            .is_err()
    );

    let m_noise = ZExpFamily::<1> { scale: rbig!(1) }.make_noise(space.clone())?;
    assert_eq!(m_noise.map(&rbig!(1))?, 1.0);
    assert!(m_noise.invoke(&vec![IBig::from(1)]).is_ok());

    Ok(())
}

#[test]
fn test_make_noise_ibig_gaussian() -> Fallible<()> {
    let space = (
        VectorDomain::new(AtomDomain::<IBig>::default()),
        L2Distance::<RBig>::default(),
    );

    assert!(
        ZExpFamily::<2> { scale: rbig!(-1) }
            .make_noise(space.clone())
            .is_err()
    );

    let m_noise = ZExpFamily::<2> { scale: rbig!(1) }.make_noise(space.clone())?;
    assert_eq!(m_noise.map(&rbig!(1))?, 0.5);
    assert!(m_noise.invoke(&vec![IBig::from(1)]).is_ok());

    Ok(())
}