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This paper describes the second part of our study devoted to automated anonymization of personal data. The overview and analysis of research prospects is supplemented with a practical result. An anonymization model is proposed, which reduces anonymization of personal data to manipulation of samples of random elements of different types. The key idea of our approach to anonymization of personal data with preservation of their usefulness is the use of the synthesis method, i.e., the complete replacement of all non-anonymized data with synthetic values. In the proposed model, a set of element types is selected, for which corresponding synthesys templates are proposed. The set of templates constitutes a synthesis-based anonymization algorithm. Technically, each template is based on a well-known statistical tool: frequency estimates of probabilities, Parzen–Rosenblatt kernel density estimates, statistical means, and covariances. The proposed approach is illustrated by a simple example from civil aviation. [ABSTRACT FROM AUTHOR]