Hiding mobile traffic fingerprints with GLOVE

@inproceedings{Gramaglia2015HidingMT,
  title={Hiding mobile traffic fingerprints with GLOVE},
  author={Marco Gramaglia and Marco Fiore},
  booktitle={CoNEXT},
  year={2015}
}
Preservation of user privacy is paramount in the publication of datasets that contain fine-grained information about individuals. The problem is especially critical in the case of mobile traffic datasets collected by cellular operators, as they feature high subscriber trajectory uniqueness and they are resistant to anonymization through spatiotemporal generalization. In this work, we first unveil the reasons behind such undesirable features of mobile traffic datasets, by leveraging an original… CONTINUE READING
Highly Cited
This paper has 25 citations. REVIEW CITATIONS

From This Paper

Figures, tables, and topics from this paper.

Citations

Publications citing this paper.
Showing 1-10 of 20 extracted citations

Beyond K-Anonymity: Protect Your Trajectory from Semantic Attack

2017 14th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) • 2017
View 9 Excerpts
Highly Influenced

A New Privacy Breach: User Trajectory Recovery From Aggregated Mobility Data

IEEE/ACM Transactions on Networking • 2018
View 4 Excerpts
Highly Influenced

You can hide, but your periodic schedule can't

2017 IEEE/ACM 25th International Symposium on Quality of Service (IWQoS) • 2017
View 8 Excerpts
Highly Influenced

Your trajectory privacy can be breached even if you walk in groups

2016 IEEE/ACM 24th International Symposium on Quality of Service (IWQoS) • 2016
View 10 Excerpts
Highly Influenced

ACCIO: How to Make Location Privacy Experimentation Open and Easy

2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS) • 2018
View 2 Excerpts

References

Publications referenced by this paper.

Similar Papers

Loading similar papers…