Structure Selection for Fairness-Constrained Differentially Private Data Synthesis
This addresses fairness-critical data release for applications like legal compliance, though it is incremental as it builds on existing DP synthesis methods.
The paper tackled the problem of generating differentially private synthetic data that also satisfies fairness constraints by enforcing conditional independence between sensitive attributes and outcomes, resulting in improved fidelity and predictive accuracy over prior methods on standard benchmarks.
Differential privacy (DP) enables safe data release, with synthetic data generation emerging as a common approach in recent years. Yet standard synthesizers preserve all dependencies in the data, including spurious correlations between sensitive attributes and outcomes. In fairness-critical settings, this reproduces unwanted bias. A principled remedy is to enforce conditional independence (CI) constraints, which encode domain knowledge or legal requirements that outcomes be independent of sensitive attributes once admissible factors are accounted for. DP synthesis typically proceeds in two phases: (i) a measure- ment step that privatizes selected marginals, often structured via minimum spanning trees (MSTs), and (ii) a reconstruction step that fits a probabilistic model consistent with the noisy marginals. We propose PrivCI, which enforces CI during the measurement step via a CI-aware greedy MST algorithm that integrates feasibility checks into Kruskal's construction under the exponential mechanism, improving accuracy over competing methods. Experiments on standard fairness benchmarks show that PrivCI achieves stronger fidelity and predictive accuracy than prior baselines while satisfying the specified CI constraints.