论文标题
通过抑制揭示有关私人属性信息的功能来培训保护隐私视频分析管道
Training privacy-preserving video analytics pipelines by suppressing features that reveal information about private attributes
论文作者
论文摘要
深层神经网络越来越多地用于场景分析，包括评估暴露于户外广告的人们的关注和反应。但是，深层神经网络提取的特征经过训练，该特征也可以预测特定的，共识的属性（例如情绪），因此也可以编码有关私人，受保护的属性（例如年龄或性别）的信息。在这项工作中，我们将重点放在推理时私人信息的泄漏上。我们考虑一个对手，访问部署神经网络层所提取的功能，并使用这些功能来预测私人属性。为了防止这种攻击的成功，我们使用混乱损失来修改网络的训练，从而鼓励提取功能，从而使对手难以准确预测私人属性。我们使用公开可用的数据集对基于图像的任务进行验证这种培训方法。结果表明，与原始网络相比，所提出的PrivateNet可以将最先进的情感识别分类器的私人信息泄漏减少2.88％，而年龄段的私人信息则降低了13.06％，对任务准确性的影响最小。
Deep neural networks are increasingly deployed for scene analytics, including to evaluate the attention and reaction of people exposed to out-of-home advertisements. However, the features extracted by a deep neural network that was trained to predict a specific, consensual attribute (e.g. emotion) may also encode and thus reveal information about private, protected attributes (e.g. age or gender). In this work, we focus on such leakage of private information at inference time. We consider an adversary with access to the features extracted by the layers of a deployed neural network and use these features to predict private attributes. To prevent the success of such an attack, we modify the training of the network using a confusion loss that encourages the extraction of features that make it difficult for the adversary to accurately predict private attributes. We validate this training approach on image-based tasks using a publicly available dataset. Results show that, compared to the original network, the proposed PrivateNet can reduce the leakage of private information of a state-of-the-art emotion recognition classifier by 2.88% for gender and by 13.06% for age group, with a minimal effect on task accuracy.
