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cs.LG2025
Practical Bayes-Optimal Membership Inference Attacks
Marcus Lassila, Johan Östman, 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…
cs.LG2024
Secure Aggregation is Not Private Against Membership Inference Attacks
Khac-Hoang Ngo, Johan Östman, Giuseppe Durisi +1
Secure aggregation (SecAgg) is a commonly-used privacy-enhancing mechanism in federated learning, affording the server access only to the aggregate of model updates while safeguard…