6 papers
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…
Subgraph Federated Learning via Spectral Methods
Javad Aliakbari, Johan Ãstman, Ashkan Panahi +1
We consider the problem of federated learning (FL) with graph-structured data distributed across multiple clients. In particular, we address the prevalent scenario of interconnecte…
Practical Bayes-Optimal Membership Inference Attacks
Marcus Lassila, Johan Ãstman, Khac-Hoang Ngo +1
We develop practical and theoretically grounded membership inference attacks (MIAs) against both independent and identically distributed (i.i.d.) data and graph-structured data. Bu…
AMLgentex: Mobilizing Data-Driven Research to Combat Money Laundering
Johan Ãstman, Edvin Callisen, Anton Chen +7
Money laundering enables organized crime by moving illicit funds into the legitimate economy. Although trillions of dollars are laundered each year, detection rates remain low beca…
Decoupled Subgraph Federated Learning
Javad Aliakbari, Johan Ãstman, Alexandre Graell i Amat
We address the challenge of federated learning on graph-structured data distributed across multiple clients. Specifically, we focus on the prevalent scenario of interconnected subg…
Soft-Decision Decoding for LDPC Code-Based Quantitative Group Testing
Marvin Xhemrishi, Johan Ãstman, Alexandre Graell i Amat
We consider the problem of identifying defective items in a population with non-adaptive quantitative group testing. For this scenario, Mashauri et al. recently proposed a low-dens…