Differentially-Private "Draw and Discard" Machine Learning

11 Jul 2018Vasyl PihurAleksandra KorolovaFrederick LiuSubhash SankuratripatiMoti YungDachuan HuangRuogu Zeng

In this work, we propose a novel framework for privacy-preserving client-distributed machine learning. It is motivated by the desire to achieve differential privacy guarantees in the local model of privacy in a way that satisfies all systems constraints using asynchronous client-server communication and provides attractive model learning properties... (read more)

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