Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10321189 | Data & Knowledge Engineering | 2015 | 16 Pages |
Abstract
We conclude by extensive experimentation where we compare the risk assessments offered by our novel measure as well as by the classical one, using well-known SDC protection methods. Those experiments validate our hypothesis that the GDBRL risk measure issues, in many cases, higher risk assessments than the classical DBRL measure. In other words, relying solely on the classical DBRL measure for risk assessment might be misleading, as the true risk may be in fact higher. Hence, we strongly recommend that the SDC community considers the new GDBRL risk measure as an additional measure when analyzing the privacy offered by SDC protection algorithms.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Javier Herranz, Jordi Nin, Pablo RodrÃguez, Tamir Tassa,