Privacy aggregated teacher ensembles (PATE) is a general machine learning framework that provides privacy-preserving for training data. However, this framework faces security risks in the distributed learning environment. Firstly, the involvement of illicit nodes in communication may lead to aggregation result inaccuracies. Secondly, the semi-honest aggregator and teacher nodes could potentially result in privacy leaks of other teacher nodes. Thirdly, the aggregation results are influenced by each teacher, and there may be poisoning attacks during the aggregation process. Fourthly, malicious aggregator may tamper with the information sent to student nodes or attempt to access relevant information about student node training labels. To address the above issues, we propose a machine learning framework with stronger security and privacy in a distributed learning environment based on principal component analysis and secures multi-party computing. The framework is subjected to security analysis and experimental validation. The security analysis establishes the framework's robustness and privacy-preserving characteristics, while experimental validation demonstrates its practical viability.

Adaptation is one of the necessary capabilities of any expert system. In a traditional expert system, the evolving environment is often treated in a static view. And the system accepts the change negatively. Our focus in this paper is to construct an adaptive CBR model which can learn continually through detecting feedbacks from the outside to partially release this. Knowledge base here is improved gradually so to enhance the system's adaptation of solving problems in dynamic environment.