2 papers
cs.LG2025
Privacy Risks in Time Series Forecasting: User- and Record-Level Membership Inference
Nicolas Johansson, Tobias Olsson, Daniel Nilsson +2
Membership inference attacks (MIAs) aim to determine whether specific data were used to train a model. While extensively studied on classification models, their impact on time seri…
cs.CR2025
Practical Feasibility of Gradient Inversion Attacks in Federated Learning
Viktor Valadi, Mattias Åkesson, Johan Östman +3
Gradient inversion attacks are often presented as a serious privacy threat in federated learning, with recent work reporting increasingly strong reconstructions under favorable exp…