Many methods of differentailly private time series pattern mining have been proposed

while in those methods

the length of sequence pattern and Laplace noise directly constrain the utility of the mining results. To address the questions caused by the global query sensitivity and lower utility of the existing works

an efficient method

called PrivTSM (differentially Private Time Series Pattern Mining) is proposed

which is based on sequence lattice for mining time series pattern with differential privacy. This method relies on the longest path strategy to truncate the original database; based on the truncated database

this

method uses the table join operation to construct a differentially private sequence lattice. Furthermore

this method uses the property of the sequence lattice structure itself to allocate privacy budget reasonably and boost the accuracy of the noisy counts. PrivTSM satisfies

ε

-differential privacy through theoretical analysis. The experimental results on real datasets show that the accuracy (TPR) and average relative error (ARE) of the PrivTSM are better than those of the N-gram and Prefix-Hybrid algorithms.