activity
20242026
most citedFair Play for Individuals, Foul Play for Groups? Auditing Anonymization's Impact on ML Fairness

1 citations · 1 across the 6 of their papers we have counts for

collaborators

12 papers

cs.CR2026

Beyond Epsilon: A Principled QIF Framework for Local Differential Privacy

Ramon G. Gonze, Natasha Fernandes, Heber H. Arcolezi +2

Local Differential Privacy (LDP) has become the de facto standard for privacy-preserving data collection in large-scale systems, in particular for the purpose of estimating frequen…

cs.LG2026

Choose Wisely and Privately: Proactive Client Selection for Fair and Efficient Federated Learning

Adda Akram Bendoukha, Heber Hwang Arcolezi, Nesrine Kaaniche +1

Federated Learning enables collaborative model training across decentralized data sources without data transfer. Averaging-based FL is limited by the presence of non-IID data, whic…

cs.CR2026

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…

cs.CR2026

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…

cs.CR2026

Understanding Disclosure Risk in Differential Privacy with Applications to Noise Calibration and Auditing (Extended Version)

Patricia Guerra-Balboa, Annika Sauer, Héber H. Arcolezi +1

Differential Privacy (DP) is widely adopted in data management systems to enable data sharing with formal disclosure guarantees. A central systems challenge is understanding how DP…

cs.CR2026

Estimating the True Distribution of Data Collected with Randomized Response

Carlos Antonio Pinzón, Ehab ElSalamouny, Lucas Massot +3

Randomized Response (RR) is a protocol designed to collect and analyze categorical data with local differential privacy guarantees. It has been used as a building block of mechanis…