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We study the problem of learning representations that are private yetinformative, i.e., provide information about intended "ally" targets whilehiding sensitive "adversary" attributes. We propose Exclusion-InclusionGenerative Adversarial Network (EIGAN), a generalized private representationlearning (PRL) architecture that accounts for multiple ally and adversaryattributes unlike existing PRL solutions. While centrally-aggregated dataset isa prerequisite for most PRL techniques, data in real-world is often siloedacross multiple distributed nodes unwilling to share the raw data because ofprivacy concerns. We address this practical constraint by developing D-EIGAN,the first distributed PRL method that learns representations at each nodewithout transmitting the source data. We theoretically analyze the behavior ofadversaries under the optimal EIGAN and D-EIGAN encoders and the impact ofdependencies among ally and adversary tasks on the optimization objective. Ourexperiments on various datasets demonstrate the advantages of EIGAN in terms ofperformance, robustness, and scalability. In particular, EIGAN outperforms theprevious state-of-the-art by a significant accuracy margin (47% improvement),and D-EIGAN's performance is consistently on par with EIGAN under differentnetwork settings.

Can we Generalize and Distribute Private Representation Learning?