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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.. code:: pycon

    >>> import numpy as np
    >>> import opendp.prelude as dp

    >>> dp.enable_features("contrib")

    >>> # Create the randomized response mechanism
    >>> m_rr = dp.m.make_randomized_response_bitvec(
    ...     dp.bitvector_domain(max_weight=4), dp.discrete_distance(), f=0.95
    ... )

    >>> # compute privacy loss
    >>> m_rr.map(1)
    0.8006676684558611

    >>> # formula is 2 * m * ln((2 - f) / f)
    >>> # where m = 4 (the weight) and f = .95 (the flipping probability)

    >>> # prepare a dataset to release, by encoding a bit vector as a numpy byte array
    >>> data = np.packbits(
    ...     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0]
    ... )
    >>> assert np.array_equal(data, np.array([0, 8, 12], dtype=np.uint8))

    >>> # roundtrip: numpy -> bytes -> mech -> bytes -> numpy
    >>> release = np.frombuffer(m_rr(data.tobytes()), dtype=np.uint8)

    >>> # compare the two bit vectors:
    >>> [int(bit) for bit in np.unpackbits(data)]
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0]
    >>> [int(bit) for bit in np.unpackbits(release)]
    [...]