差分隐私保护k- means聚类方法研究

研究了基于差分隐私保护的k-means聚类隐私保护方法。首先介绍了隐私保护数据挖掘和隐私保护聚类分析的研究现状,简单介绍了差分隐私保护的基本原理和方法。为了解决差分隐私k-means聚类方法聚类结果可用性差的问题,提出了一个新的IDP k-means聚类方法,并证明了其满足e-差分隐私保护。最后的仿真实验表明,在相同隐私保护级别下,IDP k-means聚类方法与差分隐私k-means聚类方法相比,聚类可用性得到了较大程度的提高。     

一种改进的面向差分隐私保护的k-means聚类算法

笔者介绍了差分隐私保护的研究背景、差分隐私保护的基本原理和方法,分析了k-means算法的隐私泄露问题。针对传统面向差分隐私保护k-means算法存在簇中心选取随机性导致聚类可用性较低的问题,提出一种指数加噪机制与密度估计相结合的方法,选取初始聚类中心,从而保证初始中心挑选的合理性,保障样本数据的隐私性。实验结果表明,提出的新方法可以显著提高聚类结果的可用性。     

基于差分隐私的混合位置隐私保护

针对现有差分隐私k-means算法对初始中心点敏感、用户位置数据误差偏大、可用性较低等问题,根据LBS的特点,引入人流密度的概念,提出一种基于差分隐私k-means的混合位置隐私保护方法。根据LBS特点将用户位置点分成离散位置点和非离散位置点,基于差分隐私技术,采用改进聚类算法对位置信息进行泛化和加噪;通过分析用户位置点的稀疏程度来确定离散点,对离散点位置信息采用基于差分隐私的单独加噪技术;对非离散点采用基于差分隐私的改进k-means算法进行泛化处理,以实现用户位置信息的隐私保护。仿真实验表明,在相同隐私预算的前提下,该方法具有较高的数据可用性。     

基于差分隐私保护的Stacking集成聚类算法研究

针对差分隐私保护下单一聚类算法准确性和安全性不足的问题,提出了一种基于差分隐私保护的Stacking集成聚类算法。使用Stacking集成多种异质聚类算法,将K-means聚类、Birch层次聚类、谱聚类和混合高斯聚类作为初级聚类算法,结合轮廓系数对初级聚类算法产生的聚类结果加权并入原始数据,将K-means算法作为次级聚类算法对扩展后的数据集进行聚类分析。其中,针对原始数据和初级聚类算法的聚类结果分别提出自适应的ε函数确定隐私预算,为不同敏感度的数据分配不同程度的Laplace噪声。理论分析和实验结果均表明,与单一聚类算法相比,该算法满足ε-差分隐私保护的同时有效提高了聚类准确性,实现了隐私保护与数据可用性的高度平衡。     

基于区块链的多方隐私保护k-means聚类方案

针对现有隐私保护k-means聚类方案迭代效率不高,中心化差分隐私保护k-means聚类方案中服务器会遭受攻击,以及本地化差分隐私保护k-means聚类方案中服务器会返回错误聚类结果的问题,提出了一种基于区块链的多方隐私保护k-means聚类方案（M-PPkCS/B）。利用本地化差分隐私技术的优势及区块链公开透明、不可篡改的特性,首先,设计一种多方k-means聚类中心初始化算法（M-kCCIA）,在保护用户隐私的同时,提高聚类的迭代效率,并确保用户联合产生初始聚类中心的正确性;然后,设计一种基于区块链的隐私保护k-means聚类算法（Bc-PpkCA）,并构建聚类中心更新算法的智能合约来在区块链上迭代更新聚类中心,从而保证各个用户都能得到正确的聚类结果。在数据集HTRU2和Abalone上进行实验的结果表明,在确保各个用户得到正确聚类结果的同时,两个数据集的准确率分别能达到97.53%和96.19%,M-kCCIA的平均迭代次数与随机化初始聚类中心算法RS的平均迭代次数相比,在两个数据集上分别减少了5.68次和2.75次。     

基于Spark的支持隐私保护的聚类算法

针对经典聚类方法无法应对任意背景知识下恶意攻击者在海量数据挖掘过程中的恶意攻击问题,结合差分隐私保护机制,提出一种适用于Spark内存计算框架下满足差分隐私保护的聚类算法,并从理论上证明了改进算法满足在Spark并行计算框架下的ε-差分隐私。实验结果表明,改进算法在保证聚类结果可用性前提下,具有良好的隐私保护性和满意的运行效率,在海量数据聚类分析的隐私保护挖掘中,具有很好的应用前景和价值。     

K-means ■隐私保护聚类算法

针对异常离群点对k-means ■算法的聚类精确度影响较大且在确定中心点过程中会泄露聚类数据隐私的问题,提出DPk-means ■算法。标记离群点,降低离群点对k-means ■算法聚类精确度的影响,将差分隐私应用于k-means ■聚类算法中保护聚类数据隐私。在选择聚类初始中心点和迭代求取均值中心点的过程中,应用拉普拉斯机制注入噪声,解决数据隐私泄露的问题。通过隐私预算动态变化对聚类结果准确性的影响及同类算法对比实验分析验证,DPk-means ■算法能够提供更高的隐私保护水平且保证聚类结果的准确性。     

DP-IMKP：满足个性化差分隐私的数据发布保护方法

差分隐私因能提供强大的隐私保证,广泛应用于解决数据发布中的隐私保护问题。但是经差分隐私保护后的数据注入大量噪音,降低了数据可用性,且已有方法中,针对混合属性数据集发布的隐私保护研究成果较少和存在隐私预算分配不合理的问题。因此,提出一种基于个性化隐私预算分配的差分隐私混合属性数据发布方法（DP-IMKP）。利用互信息与属性之间关联关系,提出一种敏感属性分级策略,使用户各属性重要程度得以量化,为不同级别的属性匹配对应的隐私保护程度;结合最优匹配理论,构造隐私预算与敏感属性之间的二部图,为各级敏感属性分配合理的隐私预算;结合信息熵和密度优化思想,对经典k-prototype算法中初始中心的选择和相异度度量方法进行改进,并对原始数据集进行聚类,利用各敏感属性分配的隐私预算,对聚类中心值进行差分隐私保护,防止隐私数据信息泄露。通过实验验证,DP-IMKP方法与同类方法相比,在提高数据可用性和降低数据泄露风险方面有明显优势。     

支持本地化差分隐私保护的k-modes聚类方法

如何在保护数据隐私的同时进行可用性的数据挖掘已成为热点问题。鉴于在很多实际应用场景中,很难找到一个真正可信的第三方对用户的敏感数据进行处理,文中首次提出了一种支持本地化差分隐私技术的聚类方案——LDPK-modes(Local Differential Privacy K-modes)。与传统的基于中心化差分隐私的聚类算法相比,其不再需要一个可信的第三方对数据进行收集和处理,而由用户担任数据隐私化的工作,极大地降低了第三方窃取用户隐私的可能性。用户使用满足本地d-隐私(带有距离度量的本地差分隐私技术)定义的随机响应机制对敏感数据进行扰动,第三方收集到用户扰动数据后,恢复其统计特征,生成合成数据集,并进行k-modes聚类。在聚类过程中,将数据集上频繁出现的特征分配给初始聚类中心点,进一步提高了聚类结果的可用性。理论分析和实验结果表明了LDPK-modes的隐私性和聚类可用性。     

应用BWP指标的差分隐私保护k-means算法

差分隐私是一种基于噪声扰动的隐私保护技术,针对差分隐私保护下噪声导致的聚类中心点偏移较大的问题,提出了一种基于BWP（between-within proportion）指标的差分隐私[k]-means算法。算法将聚类有效性评价指标BWP引入到隐私预算分配过程中,对传统隐私预算分配进行加权处理,在一次迭代中为不同密度分布的簇分配不同的隐私预算,从而添加不同的随机噪声。理论分析表明新算法满足[ε]-差分隐私保护。基于四个标准数据集对新算法进行了实验,实验结果表明,在聚类结果的可用性以及算法的稳定性上新算法具有优势。     

Only connect: teaching, technology and telesis

Abstract This paper describes an approach to the design of interactive multimedia materials being developed in a European Community project. The developmental process is seen as a dialogue between technologists and teachers. This dialogue is often problematic because of the differences in training, experience and culture between them. Conditions needed for fruitful dialogue are described and the generic model for learning design used in the project is explained.     

European Community policy and the market

Abstract This paper starts with some reflections on the policy considerations and priorities which are shaping European Commission (EC) research programmes. Then it attempts to position the current projects which seek to capitalise on information and communications technologies for learning in relation to these priorities and the apparent realities of the marketplace. It concludes that while there are grounds to be optimistic about the contribution EC programmes can make to the efficiency and standard of education and training, they are still too technology driven.     

一种自适应子融合集成多分类器方法

融合集成方法已经广泛应用在模式识别领域,然而一些基分类器实时性能稳定性较差,导致多分类器融合性能差,针对上述问题本文提出了一种新的基于多分类器的子融合集成分类器系统。该方法考虑在度量层融合层次之上通过对各类基多分类器进行动态选择,票数最多的类别作为融合系统中对特征向量识别的类别,构成一种新的自适应子融合集成分类器方法。实验表明,该方法比传统的分类器以及分类融合方法识别准确率明显更高,具有更好的鲁棒性。     

Avoiding semantic and temporal gaps in developing software intensive systems

Development of software intensive systems (systems) in practice involves a series of self-contained phases for the lifecycle of a system. Semantic and temporal gaps, which occur among phases and among developer disciplines within and across phases, hinder the ongoing development of a system because of the interdependencies among phases and among disciplines. Such gaps are magnified among systems that are developed at different times by different development teams, which may limit reuse of artifacts of systems development and interoperability among the systems. This article discusses such gaps and a systems development process for avoiding them.     

Designing economic np control charts: A programmed simulation approach

This paper presents control charts models and the necessary simulation software for the location of economic values of the control parameters. The simulation program is written in FORTRAN, requires only 10K of main storage, and can run on most mini and micro computers. Two models are presented - one describes the process when it is operating at full capacity and the other when the process is operating under capacity. The models allow the product quality to deteriorate to a further level before an existing out-of-control state is detected, and they can also be used in situations where no prior knowledge exists of the out-of-control causes and the resulting proportion defectives.     

The development of robot art

Going through a few examples of robot artists who are recognized worldwide, we try to analyze the deepest meaning of what is called “robot art” and the related art field definition. We also try to highlight its well-marked borders, such as kinetic sculptures, kinetic art, cyber art, and cyberpunk. A brief excursion into the importance of the context, the message, and its semiotics is also provided, case by case, together with a few hints on the history of this discipline in the light of an artistic perspective. Therefore, the aim of this article is to try to summarize the main characteristics that might classify robot art as a unique and innovative discipline, and to track down some of the principles by which a robotic artifact can or cannot be considered an art piece in terms of social, cultural, and strictly artistic interest. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Explanation and prediction: an architecture for default and abductive reasoning

Although there are many arguments that logic is an appropriate tool for artificial intelligence, there has been a perceived problem with the monotonicity of classical logic. This paper elaborates on the idea that reasoning should be viewed as theory formation where logic tells us the consequences of our assumptions. The two activities of predicting what is expected to be true and explaining observations are considered in a simple theory formation framework. Properties of each activity are discussed, along with a number of proposals as to what should be predicted or accepted as reasonable explanations. An architecture is proposed to combine explanation and prediction into one coherent framework. Algorithms used to implement the system as well as examples from a running implementation are given.     

Three Process Perspectives: Organizations, Teams, and People

This paper provides the author's personal views and perspectives on software process improvement. Starting with his first work on technology assessment in IBM over 20 years ago, Watts Humphrey describes the process improvement work he has been directly involved in. This includes the development of the early process assessment methods, the original design of the CMM, and the introduction of the Personal Software Process (PSP)^SM and Team Software Process (TSP){^SM}. In addition to describing the original motivation for this work, the author also reviews many of the problems he and his associates encountered and why they solved them the way they did. He also comments on the outstanding issues and likely directions for future work. Finally, this work has built on the experiences and contributions of many people. Mr. Humphrey only describes work that he was personally involved in and he names many of the key contributors. However, so many people have been involved in this work that a full list of the important participants would be impractical.     

基于复小波噪声方差显著修正的SAR图像去噪

提出了一种基于复小波域统计建模与噪声方差估计显著性修正相结合的合成孔径雷达(Synthetic Aperture Radar,SAR)图像斑点噪声滤波方法。该方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,然后对变换后的图像进行双树复小波变换(Dualtree Complex Wavelet Transform,DCWT),并对复数小波系数的统计分布进行建模。在此先验分布的基础上,通过运用贝叶斯估计方法从含噪系数中恢复原始系数,达到滤除噪声的目的。实验结果表明该方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪效果。     

How do children do mathematics with LOGO?

Abstract  This paper considers some results of a study designed to investigate the kinds of mathematical activity undertaken by children (aged between 8 and 11) as they learned to program in LOGO. A model of learning modes is proposed, which attempts to describe the ways in which children used and acquired understanding of the programming/mathematical concepts involved. The remainder of the paper is concerned with discussing the validity and limitations of the model, and its implications for further research and curriculum development.