Transcription of Practical Secure Aggregation for Privacy-Preserving ...
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Practical Secure Aggregation for Privacy-Preserving ...
Practical Secure Aggregationfor Privacy-Preserving Machine LearningKeith Bonawitz*, Vladimir Ivanov*, Ben Kreuter*,Antonio Marcedone ,H. Brendan McMahan*, Sarvar Patel*,Daniel Ramage*, Aaron Segal*, and Karn Mountain View, CA 94043 Tech, 2 West Loop Rd., New York, NY 100441 INTRODUCTIONM achine learning models trained on sensitive real-world datapromise improvements to everything from medical screen-ing [48] to disease outbreak discovery [39]. And the wide-spread use of mobile devices means even richer and moresensitive data is becoming available [37].However, large-scale collection of sensitive data entails particularly high-profile example of the consequences ofmishandling sensitive data occurred in 1988, when the videorental history of a nominee for the US Supreme Court waspublished without his consent [4]. The law passed in responseto that incident remains relevant today, limiting how onlinevideo streaming services can use their user data [44].
Cloud-Hosted Mobile Intelligence Federated Learning Federated Learning with Secure Aggregation Figure1:Left: Inthecloud-centricapproachtomachineintelligence ...
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