syntheticdata

Synthetic Clinical Data Generation with Privacy-Utility Validation

Overview

syntheticdata generates synthetic clinical datasets that preserve statistical properties while reducing re-identification risk. Useful for privacy-aware data sharing in multi-site clinical research.

Generation: Gaussian copula, bootstrap with noise, Laplace noise perturbation
Validation: distributional fidelity (KS), correlation preservation, discriminative accuracy
Privacy assessment: nearest-neighbor distance ratio, membership inference, attribute disclosure risk
Benchmarking: compare_methods() runs all methods on the same data; model_fidelity() measures train-on-synthetic, test-on-real predictive performance

Unlike synthpop (survey data) or simPop (census microsimulation), syntheticdata integrates generation with privacy-utility validation in a single lightweight framework oriented toward clinical research.

Synthetic data validation

Figure 1 | Synthetic data preserves statistical properties while ensuring privacy. Fisher’s iris dataset (n = 150, 4 numeric variables) synthesized via Gaussian copula. (a) Marginal density overlays: synthetic (orange) closely matches real (blue) across all variables (mean KS = 0.06). (b) Pairwise correlation preservation (Frobenius diff = 0.028). (c) Validation metrics: discriminative AUC = 0.53 (indistinguishable from random), nearest-neighbor distance ratio = 1.73 (no privacy leakage). Data: Fisher (1936) Ann. Eugenics 7:179.

Why syntheticdata?

Package	Focus	syntheticdata difference
`synthpop`	Survey/census data (CART-based)	syntheticdata targets clinical data with Gaussian copula preserving correlation structure
`simPop`	Population microsimulation	syntheticdata integrates privacy metrics (NN ratio, membership inference)
`simstudy`	Simulation for trials	syntheticdata generates from real data, not from specified distributions

The gap: no CRAN package combines generation + privacy assessment + downstream model fidelity testing in one workflow. Existing tools either generate without validating, or validate without privacy-aware metrics.

# Complete workflow in 3 lines
syn <- synthesize(clinical_data, method = "parametric")
privacy_risk(syn, sensitive_cols = c("diagnosis", "age"))
model_fidelity(syn, outcome = "readmission")

Installation

# From GitHub:
devtools::install_github("CuiweiG/syntheticdata")

# After CRAN acceptance:
install.packages("syntheticdata")

Quick start

library(syntheticdata)

# Synthesize from real clinical data
syn <- synthesize(iris, method = "parametric", seed = 42)
syn

# Validate utility and privacy
validate_synthetic(syn)

Functions

Function	Description
`synthesize()`	Generate synthetic data (parametric / bootstrap / noise)
`validate_synthetic()`	Compute utility and privacy metrics (KS, AUC, NN ratio)
`compare_methods()`	Benchmark all 3 methods on the same dataset
`privacy_risk()`	Assess re-identification risk (NN ratio, membership inference, attribute disclosure)
`model_fidelity()`	Train-on-synthetic, test-on-real predictive model comparison

Key references

Jordon J et al. (2022). Synthetic Data – what, why and how? arXiv preprint arXiv:2205.03257. doi:10.48550/arXiv.2205.03257
Snoke J et al. (2018). General and specific utility measures for synthetic data. JRSS-A 181:663. doi:10.1111/rssa.12358

License

MIT