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IEEE/ICACT20220347 Slide.32        [Big Slide]       [YouTube]	Oral Presentation
	The last protocol is POSEIDON. The paper extends SPINDLE (Privacy-Preserving Training and Evaluation for Generalized Linear Models) by building a new system (named POSEIDON) that enables privacy-preserving training and evaluation of more complex neural network models in a distributed setting through multiparty HE, providing end-to-end protection: training data for all parties, the resulting model, and evaluation data for the querier. The weights of the neural network models are always encrypted during training and evaluation.

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