Differentially Private Vertical Federated Clustering
Differentially Private Vertical Federated Clustering
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DOI：
10.14778/3583140.3583146
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Zitao Li;Tianhao Wang;Ninghui Li
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文献类型：
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作者：
Zitao Li;Tianhao Wang;Ninghui Li
在许多应用程序中，多方都拥有关于同一组用户但属性集不相交的私有数据，服务器希望利用这些数据来训练模型。为了在保护数据主体隐私的同时实现模型学习，我们需要垂直联邦学习（VFL）技术，其中数据方仅共享用于训练模型的信息，而不是私有数据。然而，确保共享信息在学习准确模型的同时保持隐私是具有挑战性的。据我们所知，本文提出的算法是差分隐私垂直联邦k -均值聚类的第一个实用解决方案，其中服务器可以获得一组具有可证明的差分隐私保证的全局中心。我们的算法假设一个不可信的中央服务器，聚合不同的私人本地中心和成员编码从本地数据方。它根据接收到的信息构建加权网格作为全球数据集的概要。通过在加权网格上运行任何k均值算法来生成最终中心。我们的网格权重估计方法使用了一种新的，轻量级的，差分私有集交集基数估计算法的基础上Flajolet-Martin草图。为了提高估计精度的设置与两个以上的数据方，我们进一步提出了一个改进版本的权重估计算法和参数调整策略，以减少最终的k -均值损失接近的中央私人设置。我们提供了理论效用分析和实验评估结果，我们的算法计算的聚类中心，并表明，我们的方法在理论上和经验上比现有技术的基础上的两个基线表现更好。
In many applications, multiple parties have private data regarding the same set of users but on disjoint sets of attributes, and a server wants to leverage the data to train a model. To enable model learning while protecting the privacy of the data subjects, we need vertical federated learning (VFL) techniques, where the data parties share only information for training the model, instead of the private data. However, it is challenging to ensure that the shared information maintains privacy while learning accurate models. To the best of our knowledge, the algorithm proposed in this paper is the first practical solution for differentially private vertical federated k -means clustering, where the server can obtain a set of global centers with a provable differential privacy guarantee. Our algorithm assumes an untrusted central server that aggregates differentially private local centers and membership encodings from local data parties. It builds a weighted grid as the synopsis of the global dataset based on the received information. Final centers are generated by running any k -means algorithm on the weighted grid. Our approach for grid weight estimation uses a novel, light-weight, and differentially private set intersection cardinality estimation algorithm based on the Flajolet-Martin sketch. To improve the estimation accuracy in the setting with more than two data parties, we further propose a refined version of the weights estimation algorithm and a parameter tuning strategy to reduce the final k -means loss to be close to that in the central private setting. We provide theoretical utility analysis and experimental evaluation results for the cluster centers computed by our algorithm and show that our approach performs better both theoretically and empirically than the two baselines based on existing techniques.