Abstract: Frequent sequential pattern mining is an exploratory problem in the field of data mining. However, directly releasing the discovered frequent patterns and the corresponding true supports may reveal the individuals privacy. The state-of-the-art solution for this problem is differential privacy, which offers a strong degree of privacy protection by adding noise. Due to the inherent sequentiality and high-dimensionality in sequences, it is challenging to apply differential privacy to frequent sequential pattern mining. To address those problems, this paper presents a differentially private method called Diff-FSPM that uses an interactive way to find frequent patterns. To reduce the impact of dimensionality, Diff-FSPM first employs the exponential mechanism to obtain the optimal sequential length for truncating each sequence in the original dataset. After that, Diff-FSPM relies on a prefix-tree to compress all the frequent patterns, and then utilizes the Laplace mechanism to perturb the true support of the frequent patterns. To efficiently allocate the privacy budget, Diff-FSPM uses the closet frequent pattern and Markov assumption to guide the mining process. Finally, Diff-FSPM adopts a post-processing technique with consistency constraint to boost the accuracy of the returned noisy support counts. We theoretically prove that Diff-FSPM satisfies ε-differential privacy, and the experimental results show that it outperforms the existing methods in terms of utility.