论文标题
引入HUBER机制，用于差异私有低级矩阵完成
Introducing the Huber mechanism for differentially private low-rank matrix completion
论文作者
论文摘要
使用敏感用户数据调用隐私保护方法，执行低排名矩阵完成。在这项工作中，我们提出了一种新型的噪声添加机制，用于保存差异隐私，其中噪声分布受Huber损失的启发，Huber损失是众所周知的稳定统计中众所周知的损失功能。在使用交替的最小二乘方法解决矩阵完成问题的同时，对现有的差异隐私机制进行了评估。我们还建议使用迭代重新加权的最小二乘算法来完成低级矩阵，并研究合成和真实数据集中不同噪声机制的性能。我们证明，所提出的机制实现了类似于拉普拉斯机制的ε-差异隐私。此外，经验结果表明，在某些情况下，Huber机制在某些情况下优于Laplacian和Gaussian，否则是可比的。
Performing low-rank matrix completion with sensitive user data calls for privacy-preserving approaches. In this work, we propose a novel noise addition mechanism for preserving differential privacy where the noise distribution is inspired by Huber loss, a well-known loss function in robust statistics. The proposed Huber mechanism is evaluated against existing differential privacy mechanisms while solving the matrix completion problem using the Alternating Least Squares approach. We also propose using the Iteratively Re-Weighted Least Squares algorithm to complete low-rank matrices and study the performance of different noise mechanisms in both synthetic and real datasets. We prove that the proposed mechanism achieves ε-differential privacy similar to the Laplace mechanism. Furthermore, empirical results indicate that the Huber mechanism outperforms Laplacian and Gaussian in some cases and is comparable, otherwise.
