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Towards Differentially Private Text Representations
[article]
2020
arXiv
pre-print
Most deep learning frameworks require users to pool their local data or model updates to a trusted server to train or maintain a global model. The assumption of a trusted server who has access to user information is ill-suited in many applications. To tackle this problem, we develop a new deep learning framework under an untrusted server setting, which includes three modules: (1) embedding module, (2) randomization module, and (3) classifier module. For the randomization module, we propose a
arXiv:2006.14170v1
fatcat:pk6hsg2kvzdhrptufq5pp2tm5e