Title: Adjustable Privacy using Autoencoder-based Learning Structure Show Chinese title

Title: 基于自编码器的学习结构的可调节隐私

Abstract: Inference centers need more data to have a more comprehensive and beneficial learning model, and for this purpose, they need to collect data from data providers. On the other hand, data providers are cautious about delivering their datasets to inference centers in terms of privacy considerations. In this paper, by modifying the structure of the autoencoder, we present a method that manages the utility-privacy trade-off well. To be more precise, the data is first compressed using the encoder, then confidential and non-confidential features are separated and uncorrelated using the classifier. The confidential feature is appropriately combined with noise, and the non-confidential feature is enhanced, and at the end, data with the original data format is produced by the decoder. The proposed architecture also allows data providers to set the level of privacy required for confidential features. The proposed method has been examined for both image and categorical databases, and the results show a significant performance improvement compared to previous methods. Show Chinese abstract

Abstract: 推理中心需要更多数据来构建更全面和有益的学习模型，为此，它们需要从数据提供者那里收集数据。 另一方面，数据提供者在隐私考虑方面对将他们的数据集传递给推理中心持谨慎态度。 在本文中，通过修改自编码器的结构，我们提出了一种能够很好地管理效用-隐私权衡的方法。 更准确地说，数据首先通过编码器进行压缩，然后使用分类器分离并消除机密和非机密特征之间的相关性。 机密特征适当结合噪声，非机密特征得到增强，并且最后通过解码器生成具有原始数据格式的数据。 所提出的架构还允许数据提供者设置对机密特征所需的隐私级别。 所提出的方法已应用于图像和分类数据库，并且结果表明与之前的方法相比有显著的性能提升。