BiSample：处理局部差分隐私缺失数据的双向抽样
BiSample: Bidirectional Sampling for Handling Missing Data with Local Differential Privacy
摘要：
局部差分隐私（LDP）是近年来研究的热点问题。在具有LDP保证的现有协议中，用户在将数据共享给聚合器之前在本地对其数据进行编码和扰动。然而，在通常的实践中，由于对不同问题的不同隐私保护偏好，用户可能不愿意回答所有问题，这导致数据丢失或数据质量损失。在本文中，我们展示了一种新的方法来解决数据扰动的挑战，考虑用户的隐私偏好。具体来说，我们首先提出了BiSample：一个双向采样技术的LDP框架值扰动。然后，我们结合BiSample机制与用户的隐私偏好丢失数据扰动。理论分析和一组数据集上的实验证实了所提出的机制的有效性。
英文摘要：
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.
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