14 citations · 32 across the 12 of their papers we have counts for
14 papers
Differentially private federated learning for localized control of infectious disease dynamics
Raouf Kerkouche, Henrik Zunker, Mario Fritz +1
In times of epidemics, swift reaction is necessary to mitigate epidemic spreading. For this reaction, localized approaches have several advantages, limiting necessary resources and…
DocMIA: Document-Level Membership Inference Attacks against DocVQA Models
Khanh Nguyen, Raouf Kerkouche, Mario Fritz +1
Document Visual Question Answering (DocVQA) has introduced a new paradigm for end-to-end document understanding, and quickly became one of the standard benchmarks for multimodal LL…
DP-2Stage: Adapting Language Models as Differentially Private Tabular Data Generators
Tejumade Afonja, Hui-Po Wang, Raouf Kerkouche +1
Generating tabular data under differential privacy (DP) protection ensures theoretical privacy guarantees but poses challenges for training machine learning models, primarily due t…
NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA
Marlon Tobaben, Mohamed Ali Souibgui, Rubèn Tito +24
The Privacy Preserving Federated Learning Document VQA (PFL-DocVQA) competition challenged the community to develop provably private and communication-efficient solutions in a fede…
Towards Biologically Plausible and Private Gene Expression Data Generation
Dingfan Chen, Marie Oestreich, Tejumade Afonja +3
Generative models trained with Differential Privacy (DP) are becoming increasingly prominent in the creation of synthetic data for downstream applications. Existing literature, how…
Privacy-Aware Document Visual Question Answering
Rubèn Tito, Khanh Nguyen, Marlon Tobaben +11
Document Visual Question Answering (DocVQA) has quickly grown into a central task of document understanding. But despite the fact that documents contain sensitive or copyrighted in…
Private and Collaborative Kaplan-Meier Estimators
Shadi Rahimian, Raouf Kerkouche, Ina Kurth +1
Kaplan-Meier estimators are essential tools in survival analysis, capturing the survival behavior of a cohort. Their accuracy improves with large, diverse datasets, encouraging dat…
Client-specific Property Inference against Secure Aggregation in Federated Learning
Raouf Kerkouche, Gergely Ács, Mario Fritz
Federated learning has become a widely used paradigm for collaboratively training a common model among different participants with the help of a central server that coordinates the…
FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations
Hui-Po Wang, Dingfan Chen, Raouf Kerkouche +1
Conventional gradient-sharing approaches for federated learning (FL), such as FedAvg, rely on aggregation of local models and often face performance degradation under differential…
Private Set Generation with Discriminative Information
Dingfan Chen, Raouf Kerkouche, Mario Fritz
Differentially private data generation techniques have become a promising solution to the data privacy challenge -- it enables sharing of data while complying with rigorous privacy…
Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Data
Shadi Rahimian, Raouf Kerkouche, Ina Kurth +1
Survival analysis or time-to-event analysis aims to model and predict the time it takes for an event of interest to happen in a population or an individual. In the medical context…
Constrained Differentially Private Federated Learning for Low-bandwidth Devices
Raouf Kerkouche, Gergely Ács, Claude Castelluccia +1
Federated learning becomes a prominent approach when different entities want to learn collaboratively a common model without sharing their training data. However, Federated learnin…
Compression Boosts Differentially Private Federated Learning
Raouf Kerkouche, Gergely Ács, Claude Castelluccia +1
Federated Learning allows distributed entities to train a common model collaboratively without sharing their own data. Although it prevents data collection and aggregation by excha…
Federated Learning in Adversarial Settings
Raouf Kerkouche, Gergely Ács, Claude Castelluccia
Federated Learning enables entities to collaboratively learn a shared prediction model while keeping their training data locally. It prevents data collection and aggregation and, t…