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Data anonymization techniques enable publication of detailed information, which permits ad hoc queries and analyses, while guaranteeing the privacy of sensitive information in the data against a variety of attacks. In this tutorial, we aim to present a unified framework of data anonymization techniques, viewed through the lens of data uncertainty. Essentially, anonymized data describes a set of possible worlds that include the original data. We show that anonymization approaches generate different working models of uncertain data, and that the privacy guarantees offered by k-anonymization and l-diversity can be naturally understood in terms of the sets of possible worlds that correspond to the anonymized data. Work in query evaluation over uncertain databases can hence be used for answering ad hoc queries over anonymized data. We identify new research problems for both the Data Anonymization and the Uncertain Data communities.