Human Mobility Data Privacy Risk Estimator

This method is a fast and flexible approach to estimate privacy risk in human mobility data. The idea is to train classifiers to capture the relation between individual mobility patterns and the level of privacy risk of individuals. We show the effectiveness of our approach by an extensive experiment on real-world GPS data in two urban areas and investigate the relations between human mobility patterns and the privacy risk of individuals.

Data and Resources
To access the resources you must log in

This item has no data

Additional Info
Field Value
Accessibility Both
AccessibilityMode Download
Attribution requirements
Availability On-Site
Basic rights Download
CreationDate 2018-02-07 11:35
Creator Pappalardo Luca,,
Dependencies on Other SW
Distribution requirements
External Identifier
Field/Scope of use Non-commercial only
Hosting Environment
License term /Not specified
Owner Pappalardo Luca,,
RelatedPaper R. Pellungrini, L. Pappalardo, F. Pratesi, A. Monreale. A data mining approach to estimate privacy risk in human mobility data. ACM Transactions on Intelligent Systems and Technology (TIST), 9(3), pp. 31:1–31:27.
Requirement of non-disclosure (confidentiality mark)
Restrictions on use
Semantic Coverage
Sublicense rights No
Territory of use World Wide
ThematicCluster Human Mobility Analytics
UsageMode as-a-Service by SoBigData Infrastructure
system:type Method
Management Info
Field Value
Author Pappalardo Luca
Maintainer Pappalardo Luca
Version 1
Last Updated 26 September 2019, 12:27 (CEST)
Created 26 September 2019, 12:27 (CEST)