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Secure Aggregation is Not Private Against Membership Inference Attacks
March 27, 2024, 4:42 a.m. | Khac-Hoang Ngo, Johan \"Ostman, Giuseppe Durisi, Alexandre Graell i Amat
cs.LG updates on arXiv.org arxiv.org
Abstract: 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 safeguarding the confidentiality of individual updates. Despite widespread claims regarding SecAgg's privacy-preserving capabilities, a formal analysis of its privacy is lacking, making such presumptions unjustified. In this paper, we delve into the privacy implications of SecAgg by treating it as a local differential privacy (LDP) mechanism for each local update. We design …
abstract aggregation analysis arxiv attacks capabilities cs.cr cs.lg federated learning inference making privacy server type updates
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