Differentially private nonlinear observer design using contraction analysis
Differentially private nonlinear observer design using contraction analysis
复制标题
使用收缩分析的差分私有非线性观测器设计
DOI：
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发表时间：
2018
影响因子：
3.9
通讯作者：
Jérôme Le Ny
中科院分区：
文献类型：
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作者：
Jérôme Le Ny
真实的实时信息处理应用程序（例如那些实现更智能的基础设施的应用程序）越来越关注分析从个人获得的隐私敏感数据。为了生成关于系统用户群体习惯的准确统计数据，这些数据可能需要通过基于模型的估计器进行处理。此外，源自流行病学或社会科学的人口动态模型往往必然是非线性的。受这些趋势的启发，本文提出了一种设计基于非线性隐私保护模型的观察器的方法，该方法依赖于加性输入或输出噪声，为提供输入数据的个体提供差分隐私保证。对于输出扰动的情况，收缩分析允许我们设计收敛的观察器，并适当地设置隐私保护噪声的水平。两个例子说明了所提出的方法：估计在社会网络中使用动态随机块模型的边缘形成概率，和症状监测依赖于流行病学模型。
Real‐time information processing applications such as those enabling a more intelligent infrastructure are increasingly focused on analyzing privacy‐sensitive data obtained from individuals. To produce accurate statistics about the habits of a population of users of a system, this data might need to be processed through model‐based estimators. Moreover, models of population dynamics, originating for example from epidemiology or the social sciences, are often necessarily nonlinear. Motivated by these trends, this paper presents an approach to design nonlinear privacy‐preserving model‐based observers, relying on additive input or output noise to give differential privacy guarantees to the individuals providing the input data. For the case of output perturbation, contraction analysis allows us to design convergent observers as well as set the level of privacy‐preserving noise appropriately. Two examples illustrate the proposed approach: estimating the edge formation probabilities in a social network using a dynamic stochastic block model, and syndromic surveillance relying on an epidemiological model.