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Confidential Federated Computations
April 17, 2024, 4:43 a.m. | Hubert Eichner, Daniel Ramage, Kallista Bonawitz, Dzmitry Huba, Tiziano Santoro, Brett McLarnon, Timon Van Overveldt, Nova Fallen, Peter Kairouz, Albe
cs.LG updates on arXiv.org arxiv.org
Abstract: Federated Learning and Analytics (FLA) have seen widespread adoption by technology platforms for processing sensitive on-device data. However, basic FLA systems have privacy limitations: they do not necessarily require anonymization mechanisms like differential privacy (DP), and provide limited protections against a potentially malicious service provider. Adding DP to a basic FLA system currently requires either adding excessive noise to each device's updates, or assuming an honest service provider that correctly implements the mechanism and only …
abstract adoption analytics anonymization arxiv basic cs.cr cs.lg data device data differential differential privacy federated learning however limitations platforms privacy processing provider service systems technology type
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