Aiming at the deficiencies of traditional Differential Evolution (DE) algorithm, low optimization accuracy and low convergence speed, a Cross-Population Differential Evolution algorithm based on Opposition-based Learning (OLCPDE) was proposed by using chaos dispersion strategy, opposition-based optimization strategy and multigroup parallel mechanism. The chaos dispersion strategy was used to generate the initial population, then the population was divided into sub-groups of the elite and the general, and a standard differential evolution strategy and a differential evolution strategy of Opposition-Based Learning (OBL) were applied to the two sub-groups respectively. Meanwhile, a cross-population differential evolution strategy was applied to further improve the accuracy and enhance population diversity for unimodal function. The sub-groups were handled through these three strategies to achieve co-evolution. After the experiments are totally run for 30 times independently, it is proven that the proposed algorithm can stably converge to the global optimal solution in 11 functions among 12 standard test functions, which is superior to other comparison algorithms. The results indicate that the proposed algorithm not only has high convergence precision but also effectively avoid trapping in local optimum.

Focusing on the issue that releasing frequent sequence patterns and the corresponding true supports may reveal the individuals' privacy when the data set contains sensitive information, a Differential Private Frequent Sequence Mining (DP-FSM) algorithm was proposed. Downward closure property was used to generate a candidate set of sequence patterns, smart truncating based technique was used to sample frequent patterns in the candidate set, and geometric mechanism was utilized to perturb the true supports of each sampled pattern. In addition, to improve the usability of the results, a threshold modification method was proposed to reduce truncation error and propagation error in mining process. The theoretical analysis show that the proposed method is ε-differentially private. The experimental results demonstrate that the proposed method has lower False Negative Rate (FNR) and Relative Support Error (RSE) than that of the comparison algorithm named PFS², thus effectively improving the accuracy of mining results.