In view of the redundancy of dataset and the risk of privacy leakage caused by the similarity of track shape when the interference track was noised and publicated by the historical track， an IGSO-SDTP （Trajectory Protection of Simplification and Differential privacy of the track data based on Improved Glowworm Swarm Optimization） was proposed. Firstly， the historical trajectory dataset was reduced based on the position salient points. Secondly， the simplified trajectory dataset was generalized and noised by combining k-anonymity and differential privacy. Finally， a weighted distance was designed to take into account the distance error and track similarity， and the weighted distance was used as the evaluation index to solve the interference track with a small weighted distance based on IGSO （Improved Glowworm Swarm Optimization） algorithm. Experimental results on multiple datasets show that compared with the RD（Differential privacy for Raw trajectory data）， SDTP（Trajectory Protection of Simplification and Differential privacy）， LIC（Linear Index Clustering algorithm）， and DPKTS（Differential Privacy based on K-means Trajectory shape Similarity）， the weighted distances obtained by IGSO-SDTP are reduced by 21.94%， 9，15%， 14.25% and 10.55%， respectively. It can be seen that the interference trajectory publicated by IGSO-SDTP has better usability and stability.

In view of the actual situations such as the preference and the delay waiting of spatial crowdsourcing users of ride-hailing in life， a task allocation method of spatial crowdsourcing based on user satisfaction utility called IGSO（Improved discrete Glowworm Swarm Optimization）-SSCTA（Spatial Crowdsourcing Task Allocation based on user Satisfaction utility） was proposed. Firstly， user satisfaction utility was defined， which was composed of user preference utility， delay waiting utility and task completion expectation. Secondly， SSCTA model was constructed based on user satisfaction utility. Thirdly， IGSO algorithm was proposed by discrete coding， the initialization of reverse learning collaboration， four improved mobile strategies， adaptive selection strategy and treatment of infeasible solutions. Finally， IGSO algorithm was used to solve the above model. Experimental results on different scale datasets show that compared with the three allocation strategies of time minimization， distance minimization and random allocation， the user satisfaction utility of the proposed method is improved by 9.64%， 11.77% and 15.70% respectively， and the proposed algorithm has better stability and convergence than the greedy algorithm and other improved glowworm algorithms.