论文标题
三角样本：使用本地差异隐私处理丢失数据的双向采样
BiSample: Bidirectional Sampling for Handling Missing Data with Local Differential Privacy
论文作者
论文摘要
当地差异隐私（LDP）最近引起了很多兴趣。在现有的有利可形保证金保证的现有协议中，用户在将其共享与聚合器共享之前对其数据进行编码和删除。但是，通常，由于对不同问题的偏好不同，用户不希望回答所有问题，这会导致数据丢失或数据质量丢失。在本文中，我们展示了一种新的方法，可以考虑用户的隐私偏好，以应对数据扰动的挑战。具体而言，我们首先提出三角样本：在LDP框架中的双向采样技术值扰动。然后，我们将三顺式机制与用户的隐私偏好相结合，以丢失数据扰动。一组数据集的理论分析和实验证实了所提出的机制的有效性。
Local differential privacy (LDP) has received much interest recently. In existing protocols with LDP guarantees, a user encodes and perturbs his data locally before sharing it to the aggregator. In common practice, however, users would prefer not to answer all the questions due to different privacy-preserving preferences for different questions, which leads to data missing or the loss of data quality. In this paper, we demonstrate a new approach for addressing the challenges of data perturbation with consideration of users' privacy preferences. Specifically, we first propose BiSample: a bidirectional sampling technique value perturbation in the framework of LDP. Then we combine the BiSample mechanism with users' privacy preferences for missing data perturbation. Theoretical analysis and experiments on a set of datasets confirm the effectiveness of the proposed mechanisms.
