Quantum system is inevitably affected by the external environment in the real world. Two controlled quantum dialogue protocols are put forward based on logical χ-type states under collective noise environment. One is against collective-dephasing noise, while the other is against collective-rotation noise. Compared with existing protocols, there exist several outstanding advantages in our proposed protocols: Firstly, the χ-type state is utilized as quantum channels, it possesses better entanglement properties than GHZ state, W state as well as cluster state, which make it difficult to be destroyed by local operations. Secondly, two kinds of logical χ-type states are constructed by us in theory, which can be perfectly immune to the effects of collective noise. Thirdly, the controller can be offline after quantum distribution and permission announcement, without waiting for all the participants to complete the information coding. Fourthly, the security analysis illuminates that our protocols can not only be free from the information leakage, but also resist against the intercept-and-resend attack, the entanglement-and-measure attack, the modification attack, the conspiring attack, and especially the dishonest controller’s attacks.

Data pricing is a key link to promote the efficient circulation of data in the market. However, the existing methods are still insufficient in terms of pertinence, dynamism and comprehensiveness. Therefore, we proposed a data pricing prediction model based on sparrow search optimization XGBoost, aiming to provide a reference for pricing decisions in data market. First, we crawled the data transaction information of Youedata.com and performed preprocessing operations such as outlier processing, one hot encoding and logarithmic transformation on the dataset; Secondly, we conducted exploratory data analysis to understand the distribution of data and their correlation. Then, we used the LASSO algorithm to select features for the dataset and constructed a data pricing prediction model based on SSA-XGBoost. Finally, we compared and analyzed it with six machine learning models including LightGBM, GBDT, MLP, KNN, LR and XGBoost. The experimental results show that in terms of the R-squared, the prediction results of the proposed SSA-XGBoost model exceed the above six models by 4.9%, 7.4%, 7.1%, 23.8%, 12.8%, and 2.3% respectively, and are superior to the state-of-the-art work. Furthermore, the evaluation results of the five indicators of MSE, RMSE, MAE, MAPE, and RMSPE are better than other models, showing higher stability.