Differentially Private Compressive K-means
Differentially Private Compressive K-means
复制标题
DOI：
10.1109/icassp.2019.8682829
复制
发表时间：
2019-05
期刊：
影响因子：
--
通讯作者：
V. Schellekens;Antoine Chatalic;F. Houssiau;Yves-Alexandre de Montjoye;L. Jacques;R. Gribonval
中科院分区：
文献类型：
--
作者：
V. Schellekens;Antoine Chatalic;F. Houssiau;Yves-Alexandre de Montjoye;L. Jacques;R. Gribonval
这项工作解决了从具有隐私保证的大量数据中学习的问题。草图学习框架提出通过将大规模数据集压缩成单个广义随机矩向量来处理大规模数据集，然后从该向量执行学习任务。我们修改了标准的素描机制，以提供不同的隐私，使用添加的拉普拉斯噪声结合子采样机制（每个时刻是从数据集的一个子集计算）。数据可以在几个传感器之间划分，每个传感器都在本地应用隐私保护机制，在重新组合时产生整个数据集的差分隐私草图。我们将此框架的k-均值聚类问题，其中提供的信号噪声比方面的机制的效用的措施，并讨论所获得的隐私效用权衡。
This work addresses the problem of learning from large collections of data with privacy guarantees. The sketched learning framework proposes to deal with the large scale of datasets by compressing them into a single vector of generalized random moments, from which the learning task is then performed. We modify the standard sketching mechanism to provide differential privacy, using addition of Laplace noise combined with a subsampling mechanism (each moment is computed from a subset of the dataset). The data can be divided between several sensors, each applying the privacy-preserving mechanism locally, yielding a differentially-private sketch of the whole dataset when reunited. We apply this framework to the k-means clustering problem, for which a measure of utility of the mechanism in terms of a signal-to-noise ratio is provided, and discuss the obtained privacy-utility tradeoff.