The detection and handling of data leakages is becoming a critical issue for organizations. To this end, data leakage solutions are usually employed by organizations to monitor network traffic and the use of portable storage devices. These solutions often produce a large number of alerts, whose analysis is time-consuming and costly for organizations. To effectively handle leakage incidents, organizations should be able to focus on the most severe incidents. Therefore, alerts need to be prioritized with respect to their severity. This work presents a novel approach for the quantification of data leakages based on their severity. The approach quantifies leakages with respect to the amount and sensitivity of the leaked information as well as the ability to identify the data subjects of the leaked information. To specify and reason on data sensitivity in an application domain, we propose a data model representing the knowledge in the domain. We validate our approach by analyzing data leakages within a healthcare environment.

Original languageEnglish
Title of host publicationData and Applications Security and Privacy XXVIII
Subtitle of host publication28th Annual IFIP WG 11.3 Working Conference, DBSec 2014, Proceedings
PublisherSpringer Verlag
Number of pages16
ISBN (Print)9783662439357
StatePublished - 2014
Externally publishedYes
Event28th Annual IFIP WG 11.3 Working Conference on Data and Applications Security and Privacy, DBSEC 2014 - Vienna, Austria
Duration: 14 Jul 201416 Jul 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8566 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference28th Annual IFIP WG 11.3 Working Conference on Data and Applications Security and Privacy, DBSEC 2014

    Research areas

  • Data Leakage Detection, Data Sensitivity Model, Severity Metrics

ID: 32864922