趁着最近手上的几个项目都告一段落,写篇文章介绍一个差分隐私(DP)数据库中常用的重要技术——Sparse Vector Technique(SVT),因为没有看到统一的中文翻译,就暂叫它SVT吧。 二、什么是SVT? 本节主要介绍SVT研究的对象以及基本想法。SVT可以看作某一种类似于Laplace和Exponential的Mechanism,
差分隐私中的Sparse Vector Technique简介：核心思想： SVT是一种差分隐私技术，旨在处理相关查询时提高效率。 它利用查询之间的相关性，通过生成一个共享的噪声对所有查询应用，同时为每个查询添加独立的噪声，以此减少总的噪声量，从而提高精度。算法流程： 噪声生成：SVT算法生成两个类型的噪声：一个共享的...
在差分隐私领域，一种重要的技术——Sparse Vector Technique（SVT）因其在处理相关查询时的高效性而备受瞩目。SVT不同于传统的Laplace和Exponential Mechanism，它针对的是判断语句而非单一的求值函数。本文将简要介绍SVT的核心思想、算法流程及其在满足差分隐私约束下的优势。SVT的关键在于利用查询之间的相关性...
A new multiple line outage identification formulation using a sparse vector recovery techniqueAC/DC power flow equationsEstimationLine outagesPMURegularized least squareThis paper develops new methods to estimate the locations of transmission line outages in transmission networks. Two transmission outage ...
We propose a greedy method, SVARGS (Sparse VAR Greedy Search), to overcome the drawbacks of standard vector auto-regression estimation techniques for sparse models. The SVARGS method is a variation on the technique described in35, and it was implemented earlier for a single variable case in a...
Sparse Matrix-Vector Multiplication refers to a fundamental computational operation used in scientific and engineering applications that involves multiplying a sparse matrix with a vector. It is a process where the nonzero elements of a sparse matrix are multiplied with the corresponding elements of a ...
The sparse coefficients of each local patch are divided into several segments, according to the template that each element of the vector corresponds to, i.e., wheredenotes the k-th segment of the coefficient vector bi. 根据向量每一个元素对应的模版,每个局部块的稀疏系数分成几个部分,例如,表示系...
SupportVectorMachines(SVM)(Vapnik,1995)arewell-known learningalgorithmscoveringmanyinterestingtopics,suchasclas- sification(Burges,1998),regression(SmolaandSchölkopf,2004), noveltydetection(Schölkopfetal.,2000),anddensityestimation (VapnikandMukherjee,1999).Anumberofextensions,e.g. (Lanckrietetal.,2002...
basic understanding of an already existing technique. The rigorous mathematical apparatus that we use can be mostly found in chapter 1 of the book of G. Wahba (1990) . 2.1 Support Vector Machines The problem we want to solve is the following: we are given a data set D = {(x i , y...
First,bothJ_{\text {photo }}andJ_{\text {geo }}are evaluated atx=0. This technique is called"First Estimate Jacobians"[11], [17], and is required to maintain consistency of the system and prevent the accumulation of spurious information. ...