Sharing data for public security

Abstract

Data sharing is a valuable tool for improving security. It allows integrating information from multiple sources to better identify and respond to global security threats. On the other side, sharing of data is limited by privacy and confidentiality. A possible solution is removing or obfuscating part of the data before release (anonymization), and, to this scope, various masking algorithms have been proposed. However, finding the right balance between privacy and the quality of data is often difficult, and it needs a fine calibration of the anonymization process. It includes choosing the 'best' set of masking algorithms and an estimation of the risk in releasing the data. Both these processes are rather complex, especially for non-expert users. In this paper, we illustrate the typical issues in the anonymization process, and introduce a tool for assisting the user in the choice of the set of masking transformations. We also propose a caching system to speed up this process over multiple runs on similar datasets. Although, the current version has limited functionalities, and more extensive testing is needed, it is a first step in the direction of developing a user-friendly support tool for anonymization.