In the process of management decision-making, the real state of the management objects is often subject to low-quality of self-reported data or large sampling biases due to concerns regarding privacy or sensitivity, which makes it difficult to know the real situation of the target objects. To solve this problem, yet to meet the data privacy protection demand in the era of the digital economy, this paper develops a data collection method based on social network indirect reports, and designs an ego-centric sampling method (ECM) based on indirectly reported sample data on the basis of network sampling and statistical inference theory. This method is simple to implement such that it can be deployed by either randomly sampling the survey objects or conducting a census. In addition to collecting the self-reported data of the samples, it also collects the report data of each sample about its close social contacts, so as to avoid the problem that they are unwilling to provide data or provide untrue data due to sensitive reasons. The proposed method can achieve a high-precision estimation of the population, and it can realize the interactive verification of self-reporting data and alter-reporting data. The research method is fully validated on the online social network of a hard-to-reach population with up to 556,627 active users. The sampling experiment shows that the estimation bias of ECM for the average number of friends and overall characteristics of the whole network is less than 3%. Furthermore, this paper conducts an empirical study by implementing a questionnaire survey on general and sensitive variables for employees in an enterprise, and achieved the overall estimation of the study objects through the indirect estimation method, verifying the practicality and effectiveness of ECM.