Local differential privacy protection for frequent sequence mining

To enhance the privacy protection of frequent sequences, improve its mining utility, and reduce the effect of data dimensionality, we propose a frequent sequence mining model that satisfies local differential privacy and design an algorithm to achieve it. The algorithm obtains frequent sequences on the basis of the idea of pruning. First, we analyzed interference in the data set using the randomized response method based on local sensitivity and utilized the sequence support degree and proprietary privacy budget to improve its applicability, and on the basis of the FP-growth prefix and suffix principle, we mined frequent sequences of level 3 and above using frequent sequences of level 2 and above. Second, we selected reasonable local sensitivity to traverse the data set before and after interference to determine the runtime of frequent sequence mining. Finally, on the basis of the combination nature of local differential privacy, we proved theoretically that the algorithm satisfies local differential privacy and verified experimentally its effectiveness. The experimental results indicate that the algorithm can implement local differential privacy protection of frequent sequences safely and efficiently, ensuring the accuracy of frequent sequences. © 2019, Editorial Department of Journal of HEU. All right reserved.