Graph-Based Data Publication via Differentially Structural Inference

Due to the rapid development of Internet of Things, a large number of data are collected and published. Nevertheless, the process of data publication entails the risk of data privacy disclosure. Most of the related works can be largely classified into data publication based on anonymity, differential privacy, and graph. However, these existing works either cannot provide theoretically provable privacy protection, or only considered one kind of data attribute and thus cannot guarantee the desirable data utility. To this end, we propose a graph-based data publication scheme via differentially structural inference that can provide theoretically provable differential privacy for individuals, and maintain desirable data utility in many practical applications rather than a certain kind of statistics or data mining results. The main idea is to map the dataset to be published into a data graph, and further use the hierarchical random graph model in statistics to encode the structure of the data graph into dendrograms. Then, we use the Markov Chain Monte Carlo to infer an optimal dendrogram, and moreover design threshold strategy to differentially disturb the optimal dendrogram. Finally, we generate the sanitized data graph based on the disturbed optimal dendrogram, and further map the sanitized data graph to the sanitized dataset to be published. Thereafter, we theoretically prove the performance boundaries of both the privacy preservation and the data utility guarantees provided in our work. Furthermore, the extensive experimental results on two real-world datasets demonstrate that the proposed scheme is superior to the existing work and Baseline, guaranteeing the data utility and preserving the data privacy in many practical applications.