ArticleMachine learning2017
Preserving differential privacy in convolutional deep belief networks.
Article in Machine learning, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Encrypted Spiking Neural Networks Based on Adaptive Differential Privacy Mechanism.Entropy (Basel, Switzerland) · 2025Article
- Privacy-preserving deep learning for pervasive health monitoring: a study of environment requirements and existing solutions adequacy.Health and technology · 2022Review
- Analysis of Application Examples of Differential Privacy in Deep Learning.Computational intelligence and neuroscience · 2021Review
- [Segmentation of organs at risk in nasopharyngeal cancer for radiotherapy using a self-adaptive Unet network].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2020Article
- Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The remarkable development of deep learning in medicine and healthcare domain presents obvious privacy issues, when deep neural networks are built on users' personal and highly sensitive data, e.g., clinical records, user profiles, biomedical images, etc. However, only a few scientific studies on preserving privacy in deep learning have been conducted. In this paper, we focus on developing a private convolutional deep belief network (pCDBN), which essentially is a convolutional deep belief network (CDBN) under differential privacy. Our main idea of enforcing
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.