Differential Privacy Data Publication Based on Scoring Function

Existing Bayesian network-based differential privacy algorithms predominantly employ uniform privacy budget allocation. However, since attribute nodes carry heterogeneous information loads, the traditional privacy budget allocation strategy may result in insufficient noise being added to important attributes, while excessive noise is added to less important attributes. To optimize privacy budget utilization, we propose SA-PrivBayes, a scoring-function-driven allocation method. To enhance Bayesian network precision, we introduce a threshold mechanism during network construction that pre-filters low-scoring attribute pairs before applying the exponential mechanism for selection. Subsequently, during parameter learning, privacy budgets are dynamically allocated to low-dimensional attribute sets based on node-specific scoring functions. Under identical privacy budgets, our algorithm demonstrates stronger data protection capabilities compared to the PrivBayes algorithm. Experimental results indicate that, compared to traditional differential privacy methods based on Bayesian networks under identical privacy budgets, our algorithm better meets the privacy protection requirements of high-dimensional data while maintaining higher data utility.