Anonymized Data: Generation, models, usage

Data anonymization techniques have been the subject of intense investigation in recent years, for many kinds of structured data, including tabular, item set and graph data. They 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, one of which corresponds to the original data. We show that anonymization approaches such as suppression, generalization, perturbation and permutation generate different working models of uncertain data, some of which have been well studied, while others open new directions for research. We demonstrate that the privacy guarantees offered by methods such as k-anonymization and -diversity can be naturally understood in terms of simil...

• Type: Conference
• Year: 2010
• Where: ICDE
• Authors: Graham Cormode, Divesh Srivastava
• Keywords: Anonymized Data, Data Anonymization, Data Anonymization Techniques, Database, ICDE 2010