39 citations · 55 across the 14 of their papers we have counts for
3 papers · 1 filter
High-Dimensional Asymptotics and Dataset Selection for Private Transfer Learning
Filip Kovačević, Edwige Cyffers, Stefano Sarao Mannelli +1
To commit to buying external data or participate in collaborative learning, one must decide whether the additional data will improve prediction enough to justify the cost. This com…
Performative Privacy: When Differential Privacy Maximizes Utility
Uddalak Mukherjee, Edwige Cyffers, Yann Chevaleyre
Privacy-preserving learning is often motivated by the idea that protecting users' data can preserve trust and thus participation, improving utility in the long term. However, this…
Limits of Personalizing Differential Privacy Budgets
Edwige Cyffers, Juba Ziani
A key technical difficulty in differential privacy is selecting a privacy budget that satisfies privacy requirements while maximizing utility. A natural and well-studied workaround…