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Data Privacy, K-Anonimity, Query Processing, Database Management Systems

Anonymization techniques are used to ensure the privacy preservation of the data owners, especially for personal and sensitive data. While in most cases, data reside inside the database management system; most of the proposed anonymization techniques operate on and anonymize isolated datasets stored outside the DBMS. Hence, most of the desired functionalities of the DBMS are lost, e.g., consistency, recoverability, and efficient querying. In this paper, we address the challenges involved in enforcing the data privacy inside the DBMS. We implement the k-anonymity algorithm as a relational operator that interacts with other query operators to apply the privacy requirements while querying the data. We study anonymizing a single table, multiple tables, and complex queries that involve multiple predicates. We propose several algorithms to implement the anonymization operator that allow efficient non-blocking and pipelined execution of the query plan. We introduce the concept of k-anonymity view as an abstraction to treat k-anonymity (possibly, with multiple k preferences) as a relational view over the base table(s). For non-static datasets, we introduce the materialized k-anonymity views to ensure preserving the privacy under incremental updates. A prototype system is realized based on PostgreSQL with extended SQL and new relational operators to support anonymity views. The prototype system demonstrates how anonymity views integrate with other privacy- preserving components, e.g., limited retention, limited disclosure, and privacy policy management. Our experiments, on both synthetic and real datasets, illustrate the performance gain from the anonymity views as well as the proposed query optimization techniques under various scenarios.