Statistical machine learning technology has rapidly developed and made significant breakthroughs in application fields. However, an attacker's intrusion can easily disrupt the learning process, leading to the system becoming unreliable. For example, in the field of precision medicine, attacks can lead to serious side effects in patients' treatment plans and leak their personal information. Trustworthy machine learning has gradually become a new and popular research direction in artificial intelligence, committed to making machine learning and trustworthy, including security, robustness, privacy, fairness, and interpretability. At present, privacy-preserving is gradually receiving attention and attention from experts and scholars in fields such as machine learning and statistics. Considering that regression and clustering are the fundamental positions of machine learning methods, this report will briefly outline the research progress of differential privacy techniques in regression and clustering, and introduce our new noise mechanism proposed in distributed learning to achieve privacy protection.