Web: http://arxiv.org/abs/2010.01792

Jan. 28, 2022, 2:11 a.m. | Sheikh Shams Azam, Taejin Kim, Seyyedali Hosseinalipour, Carlee Joe-Wong, Saurabh Bagchi, Christopher Brinton

cs.LG updates on arXiv.org arxiv.org

We study the problem of learning representations that are private yet
informative i.e., provide information about intended "ally" targets while
hiding sensitive "adversary" attributes). We propose Exclusion-Inclusion
Generative Adversarial Network (EIGAN), a generalized private representation
learning (PRL) architecture that accounts for multiple ally and adversary
attributes unlike existing PRL solutions. While centrally-aggregated dataset is
a prerequisite for most PRL techniques, data in real-world is often siloed
across multiple distributed nodes unwilling to share the raw data because of
privacy concerns. …

arxiv learning representation representation learning

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