Shuffled Differential Privacy （SDP） models can balance the degree of privacy protection at user side and the usability of published results at server side. Therefore， they are more suitable for privacy-preserving big data collection and statistical publishing scenarios. Aiming at the problems of low shuffling efficiency and insufficient shuffling process security of the existing SDP frequency estimation methods， the following work was performed： firstly， an SDP Blind Signature Algorithm （SDPBSA） was designed on the basis of optimized elliptic curve to achieve discrimination of tampered or forged information， thereby improving the security of shuffling process. Then， a Matrix Column Rearrangement Transposition （MCRT） shuffling method was proposed to realize data shuffling by random matrix column rearrangement and matrix transposition operations， thereby improving the efficiency of shuffling process. Finally， above methods were combined to construct a complete SDP frequency estimation privacy protection framework — SM-SDP （SDP based on blind Signature and Matrix column rearrangement transposition）， and its privacy and error level were analyzed theoretically. Experimental results on datasets such as Normal， Zipf， and IPUMS （Integrated Public Use Microdata Series） demonstrate that the MCRT shuffling method improves the shuffling efficiency by about 1 to 2 orders of magnitude compared to shuffling methods such as Fisher-Yates， ORShuffle （Oblivious Recursive Shuffling）， and MRS （Message Random Shuffling）； SM-SDP framework reduces the Mean Squared Error （MSE） by 2 to 11 orders of magnitude in the presence of different proportions of malicious data compared to frequency estimation methods such as mixDUMP， PSDP （Personalized Differential Privacy in Shuffle model）， and HP-SDP （Histogram Publication with SDP）.

Because user prompts often lack professionalism in specific fields and the use of terminology， it is difficult for Large Language Models （LLM） to understand the intentions accurately and generate information that meets requirements of the field. Therefore， an Automated Preference Alignment Dual-Stage Prompt Tuning （APADPT） method was proposed to solve the preference alignment problem faced by LLM when applied in vertical fields. In APADPT， the refinement adjustments of input prompts were realized by constructing a supervised fine-tuning dataset containing human preferences and using LLM for semantic analysis and evaluation of pairwise replies. After dual-stage training， the prompt optimization rules in the general field were mastered by the model， and specialized adjustments based on characteristics of the vertical fields were performed by the model. Experimental results in the medical field show that APADPT improves the preference alignment consistency of API-based LLM and open-source LLM significantly， with the average winning rate increased by 9.5% to 20.5% under the condition of the same model parameter count. In addition， this method shows good robustness and generalization ability on all the open-source models with different parameter scales， providing a new optimization strategy for the application of LLM in vertical specialized fields， and contributing to improving model performance while maintaining generalization and adaptability of the model.

According to the analysis of the problems existing in data sharing between microscopic traffic simulation system and GIS, a standard data model and a new markup language for microscopic traffic simulation (MTML) based on Geography Markup Language(GML) were presented. Then a data sharing platform based on MTML was proposed, and the architecture and implement of data sharing platform were given. It is concluded that the data sharing platform is an efficient solution for data sharing between microscopic traffic simulation system and GIS, which has advantages of openness, being platform-independent and expandability.