01373nas a2200145   4500008004100000245005000041210005000091260001300141520092300154100002301077700001901100700002101119700002001140856006701160       2017                        eng d00aPrivacy Preserving Multidimensional Profiling0 aPrivacy Preserving Multidimensional Profiling  bSpringer3 aRecently, big data had become central in the analysis of human behavior and the development of innovative services. In particular, a new class of services is emerging, taking advantage of different sources of data, in order to consider the multiple aspects of human beings. Unfortunately, these data can lead to re-identification problems and other privacy leaks, as diffusely reported in both scientific literature and media. The risk is even more pressing if multiple sources of data are linked together since a potential adversary could know information related to each dataset. For this reason, it is necessary to evaluate accurately and mitigate the individual privacy risk before releasing personal data. In this paper, we propose a methodology for the first task, i.e., assessing privacy risk, in a multidimensional scenario, defining some possible privacy attacks and simulating them using real-world datasets.1 aPratesi, Francesca1 aMonreale, Anna1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://link.springer.com/chapter/10.1007/978-3-319-76111-4_15