Differentially private nonlinear observer design using contraction analysis

摘要

Summary >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. </abstract> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> <div class="translation abstracttxt"> <span class="zhankaihshouqi fivelineshidden" id="abstract"> <span>机译：</span><Abstract Type =“Main”XML：Lang =“EN”XML：ID =“RNC4392-ABS-ABS-ABS-0001”> <标题类型=“main”>摘要</ title> >实时信息处理应用程序，例如启用更智能的基础架构的应用程序越来越关注分析从个人获得的隐私敏感数据。为了产生关于系统用户群体的习惯的准确统计信息，可能需要通过基于模型的估算器进行处理此数据。此外，人口动态的模型，源自流行病学或社会科学，往往是必然是非线性的。这篇论文推动了这些趋势，呈现了一种设计非线性隐私保留模型的观察者的方法，依赖于添加剂输入或输出噪声，为提供输入数据的个人提供差异隐私保证。对于输出扰动的情况，收缩分析允许我们设计收敛观察者，并适当地设定隐私噪声水平。两个例子说明了所提出的方法：使用动态随机块模型估计社交网络中的边缘形成概率，以及依赖于流行病学模型的综合征监视。</ p> </摘要> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> </div> <div class="record"> <h2 class="all_title" id="enpatent33" >著录项</h2> <ul> <li> <span class="lefttit">来源</span> <div style="width: 86%;vertical-align: text-top;display: inline-block;"> <a href='/journal-foreign-21736/'>《International Journal of Robust and Nonlinear Control》</a> <b style="margin: 0 2px;">|</b><span>2020年第11期</span><b style="margin: 0 2px;">|</b><span>共1页</span> </div> </li> <li> <div class="author"> <span class="lefttit">作者</span> <p id="fAuthorthree" class="threelineshidden zhankaihshouqi"> </p> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zkzz" style="display: none;">展开▼</span> </div> </li> <li> <div style="display: flex;"> <span class="lefttit">作者单位</span> <div style="position: relative;margin-left: 3px;max-width: 639px;"> <div class="threelineshidden zhankaihshouqi" id="fOrgthree"> </div> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zhdw" style="display: none;">展开▼</span> </div> </div> </li> <li > <span class="lefttit">收录信息</span> <span style="width: 86%;vertical-align: text-top;display: inline-block;"></span> </li> <li> <span class="lefttit">原文格式</span> <span>PDF</span> </li> <li> <span class="lefttit">正文语种</span> <span>eng</span> </li> <li> <span class="lefttit">中图分类</span> <span><a href="https://www.zhangqiaokeyan.com/clc/7742.html" title="自动化系统">自动化系统;</a></span> </li> <li class="antistop"> <span class="lefttit">关键词</span> <p style="width: 86%;vertical-align: text-top;"> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=differential privacy&option=203" rel="nofollow">differential privacy;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=nonlinear filtering&option=203" rel="nofollow">nonlinear filtering;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=nonlinear observer design&option=203" rel="nofollow">nonlinear observer design;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=privacy‐preserving data analysis&option=203" rel="nofollow">privacy‐preserving data analysis;</a> </p> <div class="translation"> 机译：差异隐私;非线性滤波;非线性观察者设计;保护保留数据分析; </div> </li> </ul> </div> </div> <div class="literature cardcommon"> <div class="similarity "> <h3 class="all_title" id="enpatent66">相似文献</h3> <div class="similaritytab clearfix"> <ul> <li class="active" >外文文献</li> <li >中文文献</li> <li >专利</li> </ul> </div> <div class="similarity_details"> <ul > <li> <div> <b>1. </b><a class="enjiyixqcontent" href="/journal-foreign-detail/0704023067718.html">Differentially private nonlinear observer design using contraction analysis</a> <b>[J]</b> . <span> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=&option=202" target="_blank" rel="nofollow" class="tuijian_auth tuijian_authcolor"> </a> <a href="/journal-foreign-21736/" target="_blank" rel="nofollow" class="tuijian_authcolor">International Journal of Robust and Nonlinear Control .</a> <span>2020</span><span>,第11期</span> </span> </div> <p class="zwjiyix translation" 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