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riskutility 0.1.0

Initial release: a comprehensive framework for measuring disclosure risk and data utility of anonymized and synthetic data. All measures share a consistent S3 API (print(), summary(), plot()) and feed a multivariate Risk-Utility (R-U) map.

Disclosure risk

• Attribution-based (CAP family): dcap() (reports both the raw mean CAP and the differential CAP = mean CAP minus baseline), tcap(), weap(), disco().
• ML-based: rapid() (Risk of Attribute Prediction-Induced Disclosure; random-forest default, also lm/cart/gbm/logit) with confint(), permutation test, threshold selection, synthesizer cross-validation, and six plot types.
• Distance-based (holdout): dcr(), nndr(), ims(), repu(), including the DCR-Delusion caveat and null-distribution diagnostics.
• Membership inference: domias(), nnaa(), mia_classifier().
• Classical SDC privacy models: kanonymity(), ldiversity() (distinct/entropy/recursive), tcloseness() (EMD), suda(), individual_risk(), population_uniqueness() (Pitman/Zayatz/SNB), epsilon_identifiability(), delta_presence(), hitting_rate(), singling_out(), linkability(), attacker_risk() (prosecutor/journalist/marketer), drisk().
• Record linkage: recordLinkage() with deterministic, probabilistic (Fellegi-Sunter), PRAM, predictive, random-forest, RBRL, robust-Mahalanobis, and embedding (autoencoder) methods; independent, bijective (Hungarian / GDBRL), and optimal-transport (Sinkhorn) matching; blocking and per-record accessors. All eight methods share a single re-identification-risk definition — the probability of identifying the true match within the attacker’s candidate set. For the random-forest and embedding methods, the nearest-neighbour similarity (their former risk value) is now retained in an nn_similarity diagnostic column. na_anon (ignore/match/mismatch) is honored consistently across all methods (PRAM no longer reports an artificial zero risk for records with a missing key). New options: compute_baseline = TRUE reports the no-perturbation reference risk (with risk_reduction), and expected_risk = TRUE reports a perturbation-aware expected PRAM risk over the transition distribution. User-supplied m_probs/u_probs are validated and clamped to the open interval (0,1).
• Reporting: disclosure_report() produces a comprehensive multi-metric report.

Data utility

• Propensity-score utility: propscore(), pMSE(), specks().
• Global / interval: gower(), mqs(), ci_overlap(), ci_proximity().
• Distributional and structural: compare_wasserstein(), compare_ks_test(), compare_chisq_gof(), compare_pca(), compare_embedding(), compare_correlation_matrices(), hellinger(), energy_distance(), mmd(), copula_fidelity(), tail_fidelity(), contingency_fidelity().
• Downstream / model-based: tstr() (train on synthetic, test on real), compare_feature_importance(), compare_model_performance(), regression_fidelity(), subgroup_utility().
• Information-theoretic: KLDiv(), JSDiv(), CrossEntropy(), entropy and mutual-information helpers, privacy_score().

Multivariate Risk-Utility map

• rumap(): normalized multivariate R-U evaluation with Pareto-frontier identification, internal-consistency metrics, and seven visualizations (scatter, heatmap, dot plot, parallel coordinates, radial, PCA biplot, blockwise PCA).

Integration

• synth_pair() container plus from_synthpop() and from_simPop() converters; most measures dispatch on synth_pair objects as well as plain data frames.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.