In response to the facts that most of Attribute-Based Searchable Encryption （ABSE） schemes are designed on the basis of non-national encryption algorithms and are unable to resist internal Algorithm Substitution Attack （ASA）， an SM9-based Attribute-Based Searchable Encryption with Cryptographic Reverse Firewall （SM9ABSE-CRF） was proposed. This scheme extends the SM9 algorithm to the ABSE field， realizes fine-grained data access control， and introduces Cryptographic Reverse Firewall （CRF） technology to effectively resist ASA. SM9ABSE-CRF was analyzed under the Decisional Bilinear Diffie-Hellman （DBDH） assumption， and the deployment of CRF was formally proved to maintain functionality， preserving security， and resisting exfiltration. Theoretical analysis and simulation results show that compared to the ABSE scheme providing CRF — cABKS-CRF （consistent Attribute-Based Keyword Search system with CRF）， SM9ABSE-CRF has higher security and demonstrates notable performance advantages during index and trapdoor generation phases.

Aiming at the problems of high computational complexity and poor expansibility in the mining process of partial periodic patterns from dynamic time series data， a partial periodic pattern mining algorithm for dynamic time series data combined with multi-scale theory， named MSI-PPPGrowth （Multi-Scale Incremental Partial Periodic Frequent Pattern） was proposed. In MSI-PPPGrowth， the objective multi-scale characteristics of time series data， were made full use， and the multi-scale theory was introduced in the mining process of partial periodic patterns from time series data. Firstly， both the original data after scale division and incremental time series data were used as a finer-grained benchmark scale dataset for independent mining. Then， the correlation between different scales was used to realize scale transformation， so as to indirectly obtain global frequent patterns corresponding to the dynamically updated dataset. Therefore， the repeated scanning of the original dataset and the constant adjustment of the tree structure were avoided. In which， a new frequent missing count estimation model PJK-EstimateCount was designed based on Kriging method considering the periodicity of time series to effectively estimate the frequent missing item support count in scale transformation. Experimental results show that MSI-PPPGrowth has good scalability and real-time performance. Especially for dense datasets， MSI-PPPGrowth has significant performance advantages.