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Fix README.

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Signed-off-by: Maya Anderson <mayaa@il.ibm.com>
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Maya Anderson 2023-03-20 08:29:32 +02:00
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@ -25,13 +25,13 @@ Models"[^1] and its implementation[^2]. It is based on Black-Box MIA attack usin
distances of members (training set) and non-members (holdout set) from their nearest neighbors in the synthetic dataset.
By default, the Euclidean distance is used (L2 norm), but another ``compute_distance()`` method can be provided in
configuration instead.
The area under the receiver operating characteristic curve (AUC ROC) gives the privacy risk measure.
The area under the receiver operating characteristic curve (AUC ROC) gives the privacy risk score.
Another implementation is based on the papers "Data Synthesis based on Generative Adversarial Networks"[^3] and
"Holdout-Based Fidelity and Privacy Assessment of Mixed-Type Synthetic Data"[^4], and on a variation of its reference
implementation[^5].
It is based on distances of synthetic data records from members (training set) and non-members (holdout set).
The privacy risk measure is the share of synthetic records closer to the training than the holdout dataset.
The privacy risk score is the share of synthetic records closer to the training than the holdout dataset.
By default, the Euclidean distance is used (L2 norm), but another ``compute_distance()`` method can be provided in
configuration instead.