DP-gSpan: A Pattern Growth-based Differentially Private Frequent Sub graph Mining Algorithm

Frequent subgraph mining has been one of the researches focuses in the field of data mining, which plays an important role in understanding social interaction mechanisms, urban planning, and studying the spread of diseases in social networks. Frequent subgraph mining provides a lot of valuable information. However, mining and publishing frequent subgraphs brings more and more risks of privacy leakage. In order to solve the problem of privacy leakage, the combination of frequent subgraph mining based on Apriori and differential privacy has become the mainstream method. Nevertheless, most of the existing research studies suffer from too large candidate subgraph sets, low accuracy, and low efficiency. Therefore, we propose a more secure and effective depth-first search algorithm for frequent subgraph mining under differential privacy, which is referred to as DP-gSpan. We design a heuristic truncation strategy and a new privacy budget allocation strategy to realize the reduction of the candidate set size and the rational allocation of the privacy budget. Through privacy analysis, we prove that DP-gSpan satisfies epsilon-differential privacy. Experimental results over a large number of real-world datasets prove that the performance of the proposed mechanism is better.