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• W2912015447 abstract "It is of crucial importance to simultaneously protect against sensitive attributes in data while building predictive models. In this paper, we tackle the problem of learning representations from raw data that are i) informative and predictive of desirable variables, and ii) private and protect against adversaries that attempt to recover sensitive variables. We cast this problem under the generative adversarial network (GAN) framework and design three components: an encoder, an ally that predicts the desired variables, and an adversary that predicts the sensitive ones. As a use case, we apply our approach to learn representations of raw student clickstream event data captured as they watch lecture videos in massive open online courses (MOOCs). Through experiments on a real- world dataset collected from a MOOC, we demonstrate that our method can learn a low-dimensional representation of each user that i) excels at classifying whether a user will answer a quiz question correctly, and ii) prevents an adversary from recovering each user’s identity. Our results indicate that our approach is effective in learning representations that are both informative and private." @default.
• W2912015447 created "2019-02-21" @default.
• W2912015447 creator A5012278585 @default.
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• W2912015447 date "2018-12-01" @default.
• W2912015447 modified "2023-09-26" @default.
• W2912015447 title "Learning Informative and Private Representations via Generative Adversarial Networks" @default.
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• W2912015447 doi "https://doi.org/10.1109/bigdata.2018.8622089" @default.
• W2912015447 hasPublicationYear "2018" @default.
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