In network services, there are often conflicts among privacy protection, trust, and service quality, which greatly reduces the regulatory effectiveness of networks. Using the basic method of information theory, this paper considers the optimization mechanism of trust and the balance between privacy and service utility from the perspective of quantifying information flow. First, we build a trust model based on multiple factors, such as direct trust, service trust, and trust cloud network. The weight of trust factors is determined by category diversity and information entropy theory. Second, based on statistical knowledge and information entropy, we propose a new privacy measurement model with hierarchical weights. Third, we establish a trust privacy service relationship model to realize the trade-off between privacy, service, and trust in networks. The demander can choose to establish trust, protect privacy, or obtain high-quality services according to personal preferences and needs. Fourth, we propose a privacy trust mapping relationship model and a privacy disclosure selection model. Finally, we design different experimental schemes. These simulation results show that our research can not only provide better quality of service but also better protect privacy and help interactive parties in networks establish a strong trust relationship. This research will improve the effectiveness of networks and can be applied to class II vaccine multi-agent collaborative supervision based on technology.