In the period March 2019 - August 2020 both research themes RT1 and RT2 have been pushed forward. With regards to RT1, we started the following reseach projects: RP1) the development of a theoretical (probabilistic) ground at the basis of nonparametric Bayes and nonparametric empirical Bayes methodologies for classical species sampling problems, with empahsis on large sample asymptotic properties of exchangeable random partitions and discrete random structures thereof; RP2) the extension of nonparametric Bayes and nonparametric empirical Bayes methodologies for species sampling to the more general setting of features/traits sampling models, with applications to cancer genomics and microbial ecology; RP3) the development of a nonparametric empirical Bayes methodology for classical species sampling models under the assumption of power-law data, with applications to the context of natural language processing. With regards to RT2, we started the following reseach projects: RP4) the development of a nonparametric empirical Bayes methodology for disclosure risk assessment, which is a the basis of some modern privacy preserving mechanisms; RP5) the study, and the development, of species sampling problems in the context of privatized data by means of random hashing mechanisms; RP6) the development of a comprehensive theory for goodness-of-fit tests under the framework of differential privacy and of local differential privacy. In addition to RT1 and RT2, we started a new research theme (RT3) under which we aim at investigating the use of deep neural networks, nowadays very popular, in the context of species sampling problems. In this respect, preliminary results have been produced in the context of feedforward and convolutional deep neural networks.