A High-Dimensional Temporal Data Publishing Method Based on Dynamic Bayesian Networks and Differential Privacy

Massive high-dimensional data generated by the Internet typically contains sensitive privacy information. Protecting data privacy while maintaining utility has become a pressing challenge. We propose a novel high-dimensional temporal data publishing method leveraging dynamic Bayesian networks and differential privacy. Initially, a dynamic Bayesian network is constructed, utilizing mutual information filtering of data. Subsequently, we calculate the Coherent Neighborhood Propinquity for each node within the network to determine edge sensitivity and establish a privacy budget. Noise is then strategically added to attribute data in accordance with the sensitivity and privacy budget requirements, ensuring the dataset complies with ε-differential privacy standards. Experimental results demonstrate that the data availability performance of the SMAP dataset (Soil Moisture Active Passive) surpasses that of competing algorithms while providing an equivalent level of privacy protection. Hence, our method significantly enhances data availability without compromising differential privacy protection.