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dc.contributor.authorHoefer, Nathaniel D.
dc.contributor.authorSalinas Monroy, Sergio A.
dc.date.accessioned2019-04-10T21:20:32Z
dc.date.available2019-04-10T21:20:32Z
dc.date.issued2018-12
dc.identifier.citationN. D. Hoefer and S. A. Salinas Monroy, "Performance Evaluation of a Differentially-private Neural Network for Cloud Computing," 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, 2018, pp. 2542-2545en_US
dc.identifier.isbn978-153865035-6
dc.identifier.urihttps://doi.org/10.1109/BigData.2018.8622545
dc.identifier.urihttp://hdl.handle.net/10057/16003
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractDue to the large computational cost of data classification using deep learning, resource-limited devices, e.g., smart phones, PCs, etc., offload their classification tasks to a cloud server, which offers extensive hardware resources. Unfortunately, since the cloud is an untrusted third-party, users may be reluctant to share their private data with the cloud for data classification. Differential privacy has been proposed as a way of securely classifying data at the cloud using deep learning. In this approach, users conceal their data before uploading it to the cloud using a local obfuscation deep learning model, which is based on a data classification model hosted by the cloud. However, as the obfuscation model assumes that the pre-trained model at the cloud is static, it leads to significant performance degradation under realistic classification models that are constantly being updated. In this paper, we investigate the performance of differentially-private data classification under a dynamic pre-trained model, and a constant obfuscation model. We find that the classification performance decreases as the pre-trained model evolves. We then investigate the classification performance under an obfuscation model that is updated alongside the pre-trained model. We find that with a modest computational effort the obfuscation model can be updated to significantly improve the classification performance. under a dynamic pre-trained model.en_US
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2018 IEEE International Conference on Big Data (Big Data);
dc.subjectBig dataen_US
dc.subjectClassification (of information)en_US
dc.subjectCloud computingen_US
dc.subjectDeep learningen_US
dc.subjectSmartphonesen_US
dc.titlePerformance evaluation of a differentially-private neural network for cloud computingen_US
dc.typeConference paperen_US
dc.rights.holder© 2018, IEEEen_US


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