29 citations · 129 across the 19 of their papers we have counts for
4 papers · 1 filter
Private Frequency Estimation Via Residue Number Systems
Héber H. Arcolezi
We present \textsf{ModularSubsetSelection} (MSS), a new algorithm for locally differentially private (LDP) frequency estimation. Given a universe of size and users, our $\v…
How Tough Is Location Anonymization? Re-identifying 100K Real-User Trajectories in Japan
Abhishek Kumar Mishra, Mathieu Cunche, Heber H. Arcolezi
Mobility traces are among the most revealing forms of personal data, yet trajectory releases are often protected only by ad hoc transformations. We stress-test such practices on re…
Fair Play for Individuals, Foul Play for Groups? Auditing Anonymization's Impact on ML Fairness
Héber H. Arcolezi, Mina Alishahi, Adda-Akram Bendoukha +1
Machine learning (ML) algorithms are heavily based on the availability of training data, which, depending on the domain, often includes sensitive information about data providers.…
Revisiting Locally Differentially Private Protocols: Towards Better Trade-offs in Privacy, Utility, and Attack Resistance
Héber H. Arcolezi, Sébastien Gambs
Local Differential Privacy (LDP) offers strong privacy protection, especially in settings in which the server collecting the data is untrusted. However, designing LDP mechanisms th…