Achieving Differential Privacy in Vertically Partitioned Multiparty Learning
Achieving Differential Privacy in Vertically Partitioned Multiparty Learning
复制标题
在垂直分区多方学习中实现差异隐私
DOI：
10.1109/bigdata52589.2021.9671502
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Wu, Xintao
中科院分区：
文献类型：
--
作者：
Xu, Depeng;Yuan, Shuhan;Wu, Xintao
中心化环境下的差异隐私保护已经得到了很好的研究。然而，在多方环境下，特别是在垂直分区的情况下，保护差异隐私是非常具有挑战性的。在这项工作中，我们提出了一个新的框架，用于垂直分割环境下的差分隐私保护多方学习。我们的核心思想是基于功能机制，通过在目标函数中加入噪声来实现发布模型的差分隐私。我们展示了服务器可以简单地将目标函数分解为单方和跨方子函数，并将其多项式系数的计算和扰动分配给本地方。该方法只需要一轮噪声加和安全聚合。在我们的框架中发布的模型实现了与在集中设置中应用功能机制相同的效用。对线性和逻辑回归的真实和合成数据集的评估表明了我们提出的方法的有效性。
Preserving differential privacy has been well studied under the centralized setting. However, it’s very challenging to preserve differential privacy under multiparty setting, especially for the vertically partitioned case. In this work, we propose a new framework for differential privacy preserving multiparty learning in the vertically partitioned setting. Our core idea is based on the functional mechanism that achieves differential privacy of the released model by adding noise to the objective function. We show the server can simply dissect the objective function into single-party and cross-party sub-functionsa, and allocate computation and perturbation of their polynomial coefficients to local parties. Our method needs only one round of noise addition and secure aggregation. The released model in our framework achieves the same utility as applying the functional mechanism in the centralized setting. Evaluation on real-world and synthetic datasets for linear and logistic regressions shows the effectiveness of our proposed method.
影响因子：
22.7
作者：
SHAMIR, A
通讯作者：
SHAMIR, A
处理有条件的歧视
DOI：
--
发表时间：
2011
期刊：
2011 IEEE 11th International Conference on Data Mining
影响因子：
--
作者：
Indrė Žliobaitė;F. Kamiran;T. Calders
通讯作者：
T. Calders