A Sensitivity-Aware and PSO-Driven Differential Privacy Method With Customized Budgets for Structured Data Perturbation

초록

Differential privacy (DP) is the leading standard for privacy protection, providing rigorous privacy guarantees for various data. However, its conventional approach of treating all records uniformly regarding privacy risk and using a non-adaptive privacy budget (& varepsilon;) often compromises data utility in subsequent analyses. This uniform treatment and fixed & varepsilon; can introduce significant perturbations, making the secondary use of shared data challenging. To overcome these limitations, we introduce a novel record-sensitivity-aware and Particle Swarm Optimization (PSO)-driven customised & varepsilon;-DP method for data perturbation. Our approach significantly enhances data utility without compromising privacy in data sharing by introducing three key optimisations to the traditional DP framework: First, we partition records into three sensitivity classes (high, medium, and low) based on the privacy risk. Second, we adopt a PSO mechanism to determine the optimal & varepsilon; for each partition, perturbing data with a variable & varepsilon; that considers sensitivity, rather than using a single, fixed & varepsilon; for the entire dataset. Finally, noise is injected by grouping attributes horizontally, rather than adding noise to each attribute independently, to prevent the generation of inconsistent values in the perturbed data. Detailed experiments on real benchmark and synthetic datasets demonstrate the superiority of our method in terms of utility and privacy across seven evaluation metrics, compared to the latest state-of-the-art & varepsilon;-DP methods.

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