212 citations · 505 across the 43 of their papers we have counts for
4 papers · 1 filter
Differentially Private Bayesian Inference for Generalized Linear Models
Tejas Kulkarni, Joonas Jälkö, Antti Koskela +2
Generalized linear models (GLMs) such as logistic regression are among the most widely used arms in data analyst's repertoire and often used on sensitive datasets. A large body of…
Privacy-preserving Data Sharing on Vertically Partitioned Data
Razane Tajeddine, Joonas Jälkö, Samuel Kaski +1
In this work, we introduce a differentially private method for generating synthetic data from vertically partitioned data, \emph{i.e.}, where data of the same individuals is distri…
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…
Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT
Antti Koskela, Joonas Jälkö, Lukas Prediger +1
We propose a numerical accountant for evaluating the tight -privacy loss for algorithms with discrete one dimensional output. The method is based on the privacy lo…