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.

With the rapid development of information technology, different organizations cooperate with each other to share data information and make full use of data value. Not only should the integrity and privacy of data be guaranteed but also the collaborative computing should be carried out on the basis of data sharing. In this paper, in order to achieve the fairness of data security sharing and collaborative computing, a security data collaborative computing scheme based on blockchain is proposed. A data storage query model based on Bloom filter is designed to improve the efficiency of data query sharing. The MPC contract is designed according to the specific requirements. The participants are rational, and the contract encourages the participants to implement the agreement honestly to achieve fair calculation. A secure multiparty computation based on secret sharing is introduced. The problem of identity and vote privacy in electronic voting is solved. The scheme is analyzed and discussed from storage expansion, anticollusion, verifiability, and privacy.

The emergence of edge computing has improved the real time and efficiency of the Industrial Internet of Things. In order to achieve safe and efficient data collection and application in the Industrial Internet of Things, a lot of computing and bandwidth resources are usually sacrificed. From the perspective of low computing and communication overhead, this paper proposes an efficient privacy protection layered data aggregation scheme for edge computing assisted IIoT by combining the Chinese Remainder Theorem (CRT), improved Paillier homomorphic algorithm, and hash chain technology (edge computing assisted an efficient privacy protection layered data aggregation scheme for IIoT, EE-PPDA). In EE-PPDA, first, a layered aggregation architecture based on edge computing is designed. Edge nodes and cloud are responsible for local aggregation and global aggregation, respectively, which effectively reduces the amount of data transmission. At the same time, EE-PPDA achieves data confidentiality through improved Paillier encryption, ensuring that neither attackers nor semitrusted nodes (e.g., edge nodes and clouds) can know the private data of a single device, and it can resist by simply using hash chains to resist tampering and pollution attacks ensure data integrity. Second, according to the CRT, the cloud can obtain the fine-grained aggregation results of subregions from the global aggregation results, thereby providing fine-grained data services. In addition, the EE-PPDA scheme also supports fault tolerance. Even if some IIoT devices or communication links fail, the cloud can still decrypt incomplete aggregated ciphertexts and obtain the expected aggregation results. Finally, the performance evaluation shows that the proposed EE-PPDA scheme has less calculation and communication costs.