SECRETA: A System for Evaluating and Comparing RElational and Transaction Anonymization algorithms
Giorgos Poulis, Aris Gkoulalas-Divanis, Grigorios Loukides, Spiros Skiadopoulos, Christos Tryfonopoulos · ORCA Online Research @Cardiff (Cardiff University) · 2014
Publishing data about individuals, in a privacy-preserving way, has led to a large body of research. Meanwhile, algo-rithms for anonymizing datasets, with relational or trans-action attributes, that preserve data truthfulness, have at-tracted significant interest from organizations. However, se-lecting the most appropriate algorithm is still far from triv-ial, and tools that assist data publishers in this task are needed. In response, we develop SECRETA, a system for analyzing the effectiveness and efficiency of anonymization algorithms. Our system allows data publishers to evalu-ate a specific algorithm, compare multiple algorithms, and combine algorithms for anonymizing datasets with both re-lational and transaction attributes. The analysis of the algo-rithm(s) is performed, in an interactive and progressive way, and results, including attribute statistics and various data utility indicators, are summarized and presented graphically. 1.