23 citations · 87 across the 23 of their papers we have counts for
6 papers · 1 filter
-Differential Privacy Filters: Validity and Approximate Solutions
Long Tran, Antti Koskela, Ossi Räisä +1
Accounting for privacy loss under fully adaptive composition -- where mechanism choice and privacy parameters may depend on the history of prior outputs -- is a central challenge i…
Accuracy-First Rényi Differential Privacy and Post-Processing Immunity
Ossi Räisä, Antti Koskela, Antti Honkela
The accuracy-first perspective of differential privacy addresses an important shortcoming by allowing a data analyst to adaptively adjust the quantitative privacy bound instead of…
Individual Privacy Accounting with Gaussian Differential Privacy
Antti Koskela, Marlon Tobaben, Antti Honkela
Individual privacy accounting enables bounding differential privacy (DP) loss individually for each participant involved in the analysis. This can be informative as often the indiv…
Tight Accounting in the Shuffle Model of Differential Privacy
Antti Koskela, Mikko A. Heikkilä, Antti Honkela
Shuffle model of differential privacy is a novel distributed privacy model based on a combination of local privacy mechanisms and a secure shuffler. It has been shown that the addi…
Computing Differential Privacy Guarantees for Heterogeneous Compositions Using FFT
Antti Koskela, Antti Honkela
The recently proposed Fast Fourier Transform (FFT)-based accountant for evaluating -differential privacy guarantees using the privacy loss distribution formalism h…
Differentially private cross-silo federated learning
Mikko A. Heikkilä, Antti Koskela, Kana Shimizu +2
Strict privacy is of paramount importance in distributed machine learning. Federated learning, with the main idea of communicating only what is needed for learning, has been recent…