差分隐私模糊聚类位置保护方法

针对现有差分隐私聚类位置保护方法存在初始值敏感、离散数据不适用、误差较大的问题,文中提出了一种差分隐私模糊聚类位置保护方法。首先,通过高斯核函数将点映射到特征空间,由于核函数计算量相对较小,计算效率有了显著提升;然后,将差分隐私与改进的模糊C均值聚类算法相结合,使得每一组输入数据不再仅隶属于某一特定的类,而是以隶属程度来表现;最后,文中将满足差分隐私约束的拉普拉斯噪声添加到聚类集合的质心点中,得到每个点的扰动位置,并使用扰动位置进行查询。实验结果表明,在保障位置隐私安全的前提下,差分隐私模糊聚类位置保护方法降低了查询误差,提升了算法效率。     

基于K-means方法的MapReduce模型社交网络隐私保护研究

传统隐私保护方法已逐渐无法应对多种背景下的恶意分析问题,文章研究了一种满足分布式环境下的差分隐私的算法。算法通过使用计算框架来控制主任务的迭代执行,每次分配子任务独立并行计算每个数据片中的每条记录与聚类中心的距离,标记其所属的聚类。分配子任务来计算同一簇中的记录数量和属性向量的总和,并利用机制产生的噪声干扰来实现隐私保护,从理论上证明了整个算法满足差分隐私保护。     

基于聚类匿名化的差分隐私保护数据发布方法

基于匿名化技术的理论基础,采用DBSCAN聚类算法对数据记录进行聚类,实现将个体记录匿名化隐藏于一组记录中。为提高隐私保护程度,对匿名化划分的数据添加拉普拉斯噪声,扰动个体数据真实值,以实现差分隐私保护模型的要求。通过聚类,分化查询函数敏感性,提高数据可用性。对算法隐私性进行证明,并实验说明发布数据的可用性。     

K-Modes聚类数据收集和发布过程中的混洗差分隐私保护方法

针对目前聚类数据收集与发布安全性不足的问题,为保护聚类数据中的用户隐私并提高数据质量,基于混洗差分隐私模型,提出一种去可信第三方的K-Modes聚类数据收集和发布的隐私保护方法。首先,使用K-Modes聚类数据收集算法对用户数据进行采样并加噪,再通过填补取值域随机排列发布算法打乱采样数据的初始顺序,使恶意攻击者不能根据用户与数据之间的关系识别出目标用户。然后,尽可能减小噪声的干扰,利用循环迭代的方式计算出新的质心完成聚类。最后,从理论层面上分析了以上3种方法的隐私性、可行性和复杂度,并利用3个真实数据集和近年来具有权威性的同类算法KM、DPLM、LDPKM等进行准确率、熵值的对比,验证所提方法的有效性。实验结果表明,所提方法的隐私保护和发布数据质量均优于当前同类算法。     

基于MapReduce的改进k-means文本聚类算法

针对传统k-means文本聚类算法在处理大规模文本数据时扩展性不足的问题,提出了基于MapReduce编程模型的并行k-means文本聚类算法。通过删除离群点和采用高效的初始质心选择策略提高k-means聚类效果,并设计基于MapReduce框架的大规模文本并行聚类模型提高算法的可扩展性。实验证明,该算法在大规模文本聚类中具有良好的聚类效果和可扩展性。     

基于差分隐私的权重社会网络隐私保护

针对权重社会网络发布隐私保护中的弱保护问题,提出一种基于差分隐私模型的随机扰动方法可实现边及边权重的强保护。设计了满足差分隐私的查询模型-WSQuery,WSQuery模型可捕获权重社会网络的结构,以有序三元组序列作为查询结果集;依据WSQuery模型设计了满足差分隐私的算法-WSPA,WSPA算法将查询结果集映射为一个实数向量,通过在向量中注入Laplace噪音实现隐私保护;针对WSPA算法误差较高的问题提出了改进算法-LWSPA,LWSPA算法对查询结果集中的三元组序列进行分割,对每个子序列构建满足差分隐私的算法,降低了误差,提高了数据效用。实验结果表明,提出的隐私保护方法在实现隐私信息的强保护同时使发布的权重社会网络仍具有可接受的数据效用。     

支持隐私保护的K-means聚类模型研究

K-means是一种典型的聚类算法,在机器学习领域有着重要的作用.随着外包聚类服务的发展以及用户隐私保护意识的日益提高,K-means聚类也需要对密文数据提供支持,进而保证用户数据的隐私性.为此,文中利用全同态加密(Fully Homomorphic Encryption,FHE)设计了面向加密数据的 K-means ...     

面向频繁模式挖掘的差分隐私保护研究综述

频繁模式挖掘是数据挖掘的一个基本问题,其模式本身和相应计数都有可能泄露隐私信息。当前,差分隐私通过添加噪音使数据失真,有效实现了隐私保护的目的。首先介绍了差分隐私保护模型的理论基础;其次,详细综述了差分隐私下3种典型的频繁模式挖掘方法的最新研究进展,并进行对比性分析;最后对未来的研究方向进行了展望。     

基于MapReduce和聚类算法的交通状态识别

文中提出以云计算为基础的交通状态识别方法,将经典的聚类算法通过MapReduce编程模式并行化以后,通过Hadoop平台强大的并行任务执行效果,实时监测道路的交通状态。并且改进了K-means聚类算法与模糊C均值聚类算法,用Canopy算法产生初始聚类中心,有效解决了K均值聚类和模糊C均值聚类两种算法会随机产生初始聚类中心的盲目性缺点。然后对比分析了两种改进后的聚类算法应用于交通状态识别中,选择具有更高准确率的识别方法作为最佳选择。     

Stochastic gradient descent algorithm preserving differential privacy in MapReduce framework

Aiming at the contradiction between the efficiency and privacy of stochastic gradient descent algorithm in distributed computing environment,a stochastic gradient descent algorithm preserving differential privacy based on MapReduce was proposed.Based on the computing framework of MapReduce,the data were allocated randomly to each Map node and the Map tasks were started independently to execute the stochastic gradient descent algorithm.The Reduce tasks were appointed to update the model when the sub-target update models were meeting the update requirements,and to add Laplace random noise to achieve differential privacy protection.Based on the combinatorial features of differential privacy,the results of the algorithm is proved to be able to fulfill ε-differentially private.The experimental results show that the algorithm has obvious efficiency advantage and good data availability.     

基于隐私保护的聚类研究

数据挖掘技术在发现规律和知识的同时也暴露了一些隐私信息,隐私保护因此成为数据挖掘过程中需要研究的重要问题,其目标是在不访问真实原始数据的条件下,能得到正确的数据挖掘结果.介绍聚类分析过程中的隐私保护问题,讨论了基于隐私保护的聚类分析基本思想.     

Differentially private feature selection under MapReduce framework

Privacy preserving data mining algorithms are crucial for the personal data analysis, such as medical and financial records. This paper focuses on feature selection and proposes a new privacy preserving distributed algorithm, which can effectively select features based on differential privacy and Gini index under the MapReduce framework. At the same time, the theoretic analysis for privacy guarantee is also presented. Some experiments are conducted on bench-mark datasets, the simulation results indicate that during the selection of important features, the proposed algorithm can preserve privacy information to a certain extent with less time cost than on centralized counterpart.     

CLM:differential privacy protection method for trajectory publishing

In order to solve the problem existing in differential privacy preserving publishing methods that the independent noise was easy to be filtered out,a differential privacy publishing method for trajectory data (CLM),was proposed.A correlated Laplace mechanism was presented by CLM,which let Gauss noises pass through a specific filter to produce noise whose auto-correlation function was similar with original trajectory series.Then the correlated noise was added to the original track and the perturbed track was released.The experimental results show that the proposed method can achieve higher privacy protection and guarantee better data utility compared with existing differential privacy preserving publishing methods for trajectory data.     

云计算中数据隐私保护研究进展

由于社会分工和资源共享的必然,公共云平台必将成为和电网、互联网等同等重要的国家基础设施。云计算面临的安全问题制约着云计算的广泛使用。数据安全在云计算中尤为重要,如何保证数据的安全性是云计算安全的核心。从数据的隐私保护计算、数据处理结果的完整性认证、数据访问权限控制以及数据的物理安全4个方面对已有研究工作进行了分类和总结,为后续云计算中数据的安全性研究提供参照。     

基于聚类的动态社交网络隐私保护方法

由于社交网络图结构的动态变化特性,需要采用有效的动态隐私保护方法。针对现有动态数据发布隐私保护方法中存在的攻击者背景知识单一、对图结构动态变化适应性较低等问题,提出基于聚类的动态图发布隐私保护方法。分析表明,该方法能抵御多种背景知识攻击,同时对社交网络图结构动态变化具有较好的适应性。     

DiffPRFs:random forest under differential privacy

A differential privacy algorithm DiffPRFs based on random forests was proposed.Exponential mechanism was used to select split point and split attribute in each decision tree building process,and noise was added according to Laplace mechanism.Differential privacy protection requirement was satisfied through overall process.Compared to existed algorithms,the proposed method does not require pre-discretization of continuous attributes which significantly reduces the performance cost of preprocessing in large multi-dimensional dataset.Classification is achieved conveniently and efficiently while maintains the high accuracy.Experimental results demonstrate the effectiveness and superiority of the algorithm compared to other classification algorithms.     

Survey on differential privacy and its progress

With the arrival of the era of big data sharing,data privacy protection issues will be highlighted.Since its introduction in 2006,differential privacy technology has been widely researched in data mining and data publishing.In recent years,Google,Apple and other companies have introduced differential privacy technology into the latest products,and differential privacy technology has become the focus of academia and industry again.Firstly,the traditional centralized model of differential privacy was summarized,from the perspective of analysis of data mining and data released in the differential privacy way.Then the latest local differential privacy regarding data collection and data analysis based on the local model was described,involving crowdsourcing with random response technology,BloomFilter,statistical inference techniques.Finally,the main problems and solutions of differential privacy technology were summarized.