To address the problem that the development of machine learning requires a large number of real datasets with both data security and availability， an improved K-anonymity privacy protection algorithm based on Random Forest （RF） was proposed， namely RFK-anonymity privacy protection. Firstly， the sensitivity of each attribute value was predicted by RF algorithm. Secondly， the attribute values were clustered according to different sensitivities by using the k-means clustering algorithm， and the data was hidden to different degrees by using the K-anonymity algorithm according to the sensitivity clusters of attribution. Finally， data tables with different hiding degrees were selected by different users according to their needs. Experimental results show that in Adult datasets，compared with the data processed by K-anonymity algorithm， the accuracies of the data processed by the RFK-anonymity privacy protection algorithm are increased by 0.5 and 1.6 percentage points at thresholds of 3 and 4， respectively； compared with the data processed by （p，α， k）-anonymity algorithm， the accuracies of the data processed by the proposed algorithm are improved by 0.4 and 1.9 percentage points at thresholds of 4 and 5. It can be seen that RFK-anonymity privacy protection algorithm can effectively improve the availability of data on the basis of protecting the privacy and security of data， and it is more suitable for classification and prediction in machine learning.

In order to protect data privacy while ensuring data availability in clustering analysis， a privacy protection clustering scheme based on Local Differential Privacy （LDP） technique called LDPK-Prototypes （LDP K-Prototypes） was proposed. Firstly， the hybrid dataset was encoded by users. Then， a random response mechanism was used to disturb the sensitive data， and after collecting the users’ disturbed data， the original dataset was recovered by the third party to the maximum extent. After that， the K-Prototypes clustering algorithm was performed. In the clustering process， the initial clustering center was determined by the dissimilarity measure method， and the new distance calculation formula was redefined by the entropy weight method. Theoretical analysis and experimental results show that compared with the ODPC （Optimizing and Differentially Private Clustering） algorithm based on the Centralized Differential Privacy （CDP） technique， the proposed scheme has the average accuracy on Adult and Heart datasets improved by 2.95% and 12.41% respectively， effectively improving the clustering usability. Meanwhile， LDPK-Prototypes expands the difference between data， effectively avoids local optimum， and improves the stability of the clustering algorithm.

The combat capability of the weapon system-of-systems is an important factor of the unit combat capability, so it is a commonly used key index in analyzing the structure of the unit and evaluating the unit’s capability of completing a certain mission. This system used hierarchical, composable, and entity-oriented data structure to store the weapon system-of-systems, then calculated the capabilities layer by layer of the weapon component, weapon system and weapon system-of-system based on the fire power, maneuver, supply, defense, and intelligence ability (for short "five kind capabilities") and the quantification model and aggregation model. The system preserves the numerical analysis method’s virtue of easy usage and rapid calculation, and gives the solution of aggregating and comparing different weapons’ capability. Its results can be used as reference data for formulating weapon development plan and designing combat training simulation system.