论文标题
产生的对抗用户隐私在有损失的单人服务信息检索中
Generative Adversarial User Privacy in Lossy Single-Server Information Retrieval
论文作者
论文摘要
我们建议通过在检索过程中造成扭曲并同时放松完美的隐私要求来扩展私人信息检索的概念。特别是，我们研究了下载率，失真和用户隐私泄漏之间的权衡，并表明在大型文件尺寸的限制中，可以通过针对具有已知分布的数据集来捕获此权衡的折衷。此外，对于未知数据集统计数据的方案，我们通过利用生成的对抗网络方法提出了一个新的深度学习框架，该方法允许用户从数据本身中学习有效的方案。我们在合成高斯数据集以及MNIST，CIFAR-10和LSUN数据集上评估了该方案的性能。对于MNIST，CIFAR-10和LSUN数据集，数据驱动的方法显着优于基于非学习的方案，该方案将源编码与下载多个文件结合在一起。
We propose to extend the concept of private information retrieval by allowing for distortion in the retrieval process and relaxing the perfect privacy requirement at the same time. In particular, we study the trade-off between download rate, distortion, and user privacy leakage, and show that in the limit of large file sizes this trade-off can be captured via a novel information-theoretical formulation for datasets with a known distribution. Moreover, for scenarios where the statistics of the dataset is unknown, we propose a new deep learning framework by leveraging a generative adversarial network approach, which allows the user to learn efficient schemes from the data itself. We evaluate the performance of the scheme on a synthetic Gaussian dataset as well as on the MNIST, CIFAR-10, and LSUN datasets. For the MNIST, CIFAR-10, and LSUN datasets, the data-driven approach significantly outperforms a nonlearning-based scheme which combines source coding with the download of multiple files.
