Smart Objects and Technologies for Social Good. Third International Conference, GOODTECHS 2017, Pisa, Italy, November 29-30, 2017, Proceedings

Research Article

Privacy Preserving Multidimensional Profiling

  • @INPROCEEDINGS{10.1007/978-3-319-76111-4_15,
        author={Francesca Pratesi and Anna Monreale and Fosca Giannotti and Dino Pedreschi},
        title={Privacy Preserving Multidimensional Profiling},
        proceedings={Smart Objects and Technologies for Social Good. Third International Conference, GOODTECHS 2017, Pisa, Italy, November 29-30, 2017, Proceedings},
        proceedings_a={GOODTECHS},
        year={2018},
        month={3},
        keywords={Privacy risk assessment Mobile phone data Retail data},
        doi={10.1007/978-3-319-76111-4_15}
    }
    
  • Francesca Pratesi
    Anna Monreale
    Fosca Giannotti
    Dino Pedreschi
    Year: 2018
    Privacy Preserving Multidimensional Profiling
    GOODTECHS
    Springer
    DOI: 10.1007/978-3-319-76111-4_15
Francesca Pratesi,*, Anna Monreale1, Fosca Giannotti2, Dino Pedreschi1
  • 1: University of Pisa
  • 2: ISTI-CNR A. Faedo
*Contact email: francesca.pratesi@isti.cnr.it

Abstract

Recently, 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.