审核不同隐私神经网络模型的隐私预算

IF 5.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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引用次数: 0

摘要

近年来,神经网络模型被广泛应用于各种任务中。为了消除对隐私的担忧,人们在神经网络模型的训练阶段引入了差分隐私(DP)。然而,在神经网络模型中引入 DP 非常微妙且容易出错,导致一些差异化隐私神经网络模型可能无法实现所声称的隐私保证。在本文中,我们提出了一种方法,可以审核差异化隐私神经网络模型的隐私预算。本文提出的方法具有通用性,可用于审核其他人工智能模型。我们首先阐述了如何利用所提出的方法审核基本 DP 机制和神经网络模型的隐私预算。然后,我们通过实验来验证我们的方法。实验结果表明,当隐私预算较小时,建议的方法优于先进的方法,且审计精度较高。特别是,当对 ResNet-18 的隐私预算进行审计时,在理论隐私预算为 0.2 的情况下,在 CIFAR-10 上使用差异化隐私机制保护的 ResNet-18,我们的方法的精确度约为先进方法的 17 倍。对于更简单的数据集 FMNIST,当理论隐私预算为 0.2 时,我们的方法的准确率约为最新方法的 32 倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Auditing privacy budget of differentially private neural network models
In recent years, neural network models are used in various tasks. To eliminate privacy concern, differential privacy (DP) is introduced to the training phase of neural network models. However, introducing DP into neural network models is very subtle and error-prone, resulting in that some differentially private neural network models may not achieve privacy guarantee claimed. In this paper, we propose a method, which can audit privacy budget of differentially private neural network models. The proposed method is general and can be used to audit some other AI models. We elaborate on how to audit privacy budget of basic DP mechanisms and neural network models by the proposed method first. Then, we run experiments to verify our method. Experiment results indicate that the proposed method is better than the advanced method and the auditing precise is high when the privacy budget is small. In particular, when auditing privacy budget of ResNet-18 over CIFAR-10 protected by the differentially private mechanism with theoretical privacy budget 0.2, the accuracy of our method is about 17 times that of the state-of-the-art method. For the simpler dataset FMNIST, the accuracy of our method is about 32 times that of the state-of-the-art method when theoretical privacy budget is 0.2.
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来源期刊
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
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