2 citations · 3 across the 8 of their papers we have counts for
9 papers
Efficient Provably Private Classification with a Tabular Foundation Model
Talal Alrawajfeh, Cristiana Diaconu, Ossi Räisä +5
Tabular data underpin prediction and decision-making in medicine, finance, government and science, but often contain sensitive individual-level information, creating a need for acc…
On Reliability of Membership Inference Vulnerability Evaluation
Joonas Jälkö, Gauri Pradhan, Ossi Räisä +1
Membership inference attacks (MIAs) are popular methods for empirically assessing the leakage of sensitive information in the training data through models or statistics learned fro…
-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…
Noise-Aware Differentially Private Regression via Meta-Learning
Ossi Räisä, Stratis Markou, Matthew Ashman +4
Many high-stakes applications require machine learning models that protect user privacy and provide well-calibrated, accurate predictions. While Differential Privacy (DP) is the go…
Subsampling is not Magic: Why Large Batch Sizes Work for Differentially Private Stochastic Optimisation
Ossi Räisä, Joonas Jälkö, Antti Honkela
We study how the batch size affects the total gradient variance in differentially private stochastic gradient descent (DP-SGD), seeking a theoretical explanation for the usefulness…
A Bias-Variance Decomposition for Ensembles over Multiple Synthetic Datasets
Ossi Räisä, Antti Honkela
Recent studies have highlighted the benefits of generating multiple synthetic datasets for supervised learning, from increased accuracy to more effective model selection and uncert…
On Consistent Bayesian Inference from Synthetic Data
Ossi Räisä, Joonas Jälkö, Antti Honkela
Generating synthetic data, with or without differential privacy, has attracted significant attention as a potential solution to the dilemma between making data easily available, an…
Noise-Aware Statistical Inference with Differentially Private Synthetic Data
Ossi Räisä, Joonas Jälkö, Samuel Kaski +1
While generation of synthetic data under differential privacy (DP) has received a lot of attention in the data privacy community, analysis of synthetic data has received much less.…