Privacy-Preserving Publishing of Hierarchical Data

Many applications today rely on storage and management of semi-structured information, for example, XML databases and document-oriented databases. These data often have to be shared with untrusted third parties, which makes individuals' privacy a fundamental problem. In this article, we propose anonymization techniques for privacy-preserving publishing of hierarchical data. We show that the problem of anonymizing hierarchical data poses unique challenges that cannot be readily solved by existing mechanisms. We extend two standards for privacy protection in tabular data (k-anonymity and l-diversity) and apply them to hierarchical data. We present utility-aware algorithms that enforce these definitions of privacy using generalizations and suppressions of data values. To evaluate our algorithms and their heuristics, we experiment on synthetic and real datasets obtained from two universities. Our experiments show that we significantly outperform related methods that provide comparable privacy guarantees.