Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.datak.2019.101786 | Data & Knowledge Engineering |
Keywords | Field | DocType |
Mobile phone data,Call detail record,Privacy,Anonymization | Data science,Sociometer,Official statistics,Prudence,Information retrieval,Inference,Computer science,Risk assessment,Risk management,Mobile phone,Private information retrieval | Journal |
Volume | Issue | ISSN |
125 | C | 0169-023X |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francesca Pratesi | 1 | 27 | 7.41 |
| Lorenzo Gabrielli | 2 | 109 | 10.41 |
| Paolo Cintia | 3 | 48 | 6.07 |
| Anna Monreale | 4 | 581 | 42.49 |
| Fosca Giannotti | 5 | 2948 | 253.39 |