Abstract
The availability of various large-scale datasets benefits the advancement of deep learning. These datasets are often crowdsourced from individual users and contain private information like gender, age, etc. Due to rich private information embedded in the raw data, users raise the concerns on privacy leakage from the shared data. Such privacy concerns will hinder the generation or use of crowdsourcing datasets and lead to hunger of training data for new deep learning applications. In this work, we present TAP, a task-agnostic privacy-preserving representation learning framework to protect data privacy with anonymized intermediate representation. The goal of this framework is to learn a feature extractor that can hide the privacy information from the intermediate representations; while maximally retaining the original information embedded in the raw data for the data collector to accomplish unknown learning tasks. We adopt the federated learning paradigm to train the feature extractor, such that learning the extractor is also performed in a privacy-respecting fashion. We extensively evaluate TAP and compare it with existing methods using two image datasets and one text dataset. Our results show that TAP can offer a good privacy-utility tradeoff.
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Acknowledgement
This work was supported in part by NSF-1822085 and NSF IUCRC for ASIC membership from Ergomotion. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF and their contractors.
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Li, A., Yang, H., Chen, Y. (2020). Task-Agnostic Privacy-Preserving Representation Learning via Federated Learning. In: Yang, Q., Fan, L., Yu, H. (eds) Federated Learning. Lecture Notes in Computer Science(), vol 12500. Springer, Cham. https://doi.org/10.1007/978-3-030-63076-8_4
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