PRIMULE: Privacy risk mitigation for user profiles

The availability of mobile phone data has encouraged the development of different data-driven tools, supporting social science studies and providing new data sources to the standard official statistics. However, this particular kind of data are subject to privacy concerns because they can enable the...

Full description

Saved in:
Bibliographic Details
Published in:Data & knowledge engineering 2020-01, Vol.125, p.101786, Article 101786
Main Authors: Pratesi, Francesca, Gabrielli, Lorenzo, Cintia, Paolo, Monreale, Anna, Giannotti, Fosca
Format: Article
Language:English
Subjects:
Anonymization
Call detail record
Mobile phone data
Privacy
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The availability of mobile phone data has encouraged the development of different data-driven tools, supporting social science studies and providing new data sources to the standard official statistics. However, this particular kind of data are subject to privacy concerns because they can enable the inference of personal and private information. In this paper, we address the privacy issues related to the sharing of user profiles, derived from mobile phone data, by proposing PRIMULE, a privacy risk mitigation strategy. Such a method relies on PRUDEnce (Pratesi et al., 2018), a privacy risk assessment framework that provides a methodology for systematically identifying risky-users in a set of data. An extensive experimentation on real-world data shows the effectiveness of PRIMULE strategy in terms of both quality of mobile user profiles and utility of these profiles for analytical services such as the Sociometer (Furletti et al., 2013), a data mining tool for city users classification.
ISSN:0169-023X
1872-6933
DOI:10.1016/j.datak.2019.101786