Frequent Pattern Mining with Differential Privacy Based on Transaction Truncation
Existing frequent pattern mining with the e-differential privacy model has shortcoming of its greater global sensitivity and lower availability issues. We proposed a newfrequent pattern mining with differential privacy algorithm based on the transaction truncation. The algorithm firstly used the idea of transaction truncation based on exponential mechanism,truncating the long transaction to reduce the global sensitivity. Besides,we also proposed frequent pattern mining with differential privacy based on transaction truncation on this algorithm,and then we put forward the minimum noise support standard to expand the candidate set of the Apriori algorithm,which enhances the availability of data. Experiments compared and analyzed our algorithm results with similar algorithms. Experimental results showthat the algorithm can meet the e-differential privacy and ensure high availability of mining results at the same time.