Benchmarking Differentially Private Residual Networks for Medical Imagery
By Sahib Singh, Harshvardhan Digvijay Sikka. 2020
9 citations recorded by the source corpus.
In this paper we measure the effectiveness of ϵ-Differential Privacy (DP) when applied to medical imaging. We compare two robust differential privacy mechanisms: Local-DP and DP-SGD and benchmark their performance when analyzing medical imagery records. We analyze the trade-off between the model's accuracy and the level of privacy it guarantees, and also take a closer look to evaluate how useful these theoretical privacy guarantees actually prove to be in the real world medical setting.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Praneeth Vepakomma
- Abhishek Singh
- Ramesh Raskar
- Hadi Esmaeilzadeh
- Fatemehsadat Mirshghallah
- Mohammadkazem Taram
- Otkrist Gupta
- Chaoyang He
Citations in the corpus
- Privacy in Deep Learning: A Survey (2020, background): “ of Deep Neural Networks (DNNs) in various fields including vision, medicine, recommendation systems, natural language processing, etc., has resulted in their deployment in numerous production systems [1, 2, 3, 4]. In the world of medicine, learning is used to find patterns in patient histories and to”