In today's era of rapid digitalization, industries with high data security demands increasingly rely on reliable systems. The requirements for data security and privacy protection in their operations have become more prominent. Although federated learning offers advantages in data privacy protection and collaborative modeling, it still faces privacy risks during model iteration and training interference. It has become an urgent challenge for industries with high data security requirements to build a sensitive information protection system to ensure security and efficiency of data processing. To address these challenges, we propose BloP, a trusted computing scheme that integrates blockchain with privacy-preserving computation. The scheme combines blockchain algorithms with various privacy-preserving computation technologies. BloP relies on trusted computing and measurement modules to maintain a set of trusted nodes, monitor trusted anomaly events, and establish a tamper-proof mechanism. In addition, BloP employs the PBFT consensus algorithm to accelerate the blockchain algorithm. BloP has been implemented and tested on 10 industry systems with high data security demands. During 17 months, the system detected 32,220 trusted anomaly events and 479 tamper-proof events. Furthermore, more than 90 % of these trusted anomaly events were caused by operational errors, while the rest were malicious attacks or unknown incidents.

Quantum computing is expected to introduce the next era of computing speed and power, and its software - quantum program is gaining increasing research interest in the software engineering community. A significant characteristic of quantum computing is the existence of noise. Unlike classical computers where the output of a program is usually deterministic, the execution of a quantum program may be affected by quantum noise. Such a difference may cause difficulties or misunderstandings for developers shifting from classical programming to quantum programming. To understand the impact of quantum noise on quantum programs and its implications for software developers, we conduct a series of studies with real-world quantum programs and quantum computing environments. Specifically, we first measure and analyze the noise in a real quantum computer by testing it with a basic quantum program. We find that a non-neglectable amount of quantum noise generally exists in real quantum computers. Then we investigate the robustness of quantum programs against different quantum noises by testing 18 real-world quantum programs and 50,000 randomly generated quantum circuits in simulated and real environments. We observe that quantum noise can significantly influence the correctness of quantum programs, and different quantum circuit structures show diverse sensitivity patterns under the same noise. Based on the observations, we build a machine learning model to predict the fidelity of a quantum program under certain quantum noise. The model achieves a small average fidelity prediction error, meaning the impact of noise can be precisely estimated statistically.