@inproceedings{2aedf245153240179054e901ac9ca6de,
title = "Adaptive laplace mechanism: Differential privacy preservation in deep learning",
abstract = "In this paper, we focus on developing a novel mechanism to preserve differential privacy in deep neural networks, such that: (1) The privacy budget consumption is totally independent of the number of training steps; (2) It has the ability to adaptively inject noise into features based on the contribution of each to the output; and (3) It could be applied in a variety of different deep neural networks. To achieve this, we figure out a way to perturb affine transformations of neurons, and loss functions used in deep neural networks. In addition, our mechanism intentionally adds 'more noise' into features which are 'less relevant' to the model output, and vice-versa. Our theoretical analysis further derives the sensitivities and error bounds of our mechanism. Rigorous experiments conducted on MNIST and CIFAR-10 datasets show that our mechanism is highly effective and outperforms existing solutions.",
keywords = "Deep Learning, Differential Privacy, Laplace Mechanism",
author = "Nhathai Phan and Xintao Wu and Han Hu and Dejing Dou",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 17th IEEE International Conference on Data Mining, ICDM 2017 ; Conference date: 18-11-2017 Through 21-11-2017",
year = "2017",
month = dec,
day = "15",
doi = "10.1109/ICDM.2017.48",
language = "English (US)",
series = "Proceedings - IEEE International Conference on Data Mining, ICDM",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "385--394",
editor = "Vijay Raghavan and Srinivas Alu and George Karypis and Lucio Miele and Xindong Wu",
booktitle = "Proceedings - 17th IEEE International Conference on Data Mining, ICDM 2017",
address = "United States",
}